Manual CMOS Imagers: From Phototransduction to Image Processing (Fundamental Theories of Physics)

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Stevens, H. Komori, H. Doan, H. Fujita, J. Kyan, C. Parks, G. Shi, V. Tivarus, and J. Patent 7 B2, Mar. Further improvement in the spatial resolution can be achieved [10] I. Shcherback, A. Belenky, and O. Further power decrease can be achieved by using of low-power [11] O. Yang and A. Smaller capacitance levels up the sensor sensitivity, , Jan. However, [13] S. Chamberlain and J. Circuits Conf. Consequently, CDS procedure, [14] K. Boahen and A. Labonne, G. Sicard, M. Renaudin, and P. Generally, there are three approaches for widening DR non- [16] S.

Kavadias, B. Dierickx, D. Scheffer, A. Alaerts, D. Uwaerts, and linear, piecewise linear, and linear according to the sensor J. Joseph and S. Lai, C. Lai, and Y. Frequency-based sensors, which have a linear response, reach a [19] N. Akahane, R. Ryuzaki, S.

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Adachi, K. Mizobuchi, and S. IEEE Int. Solid-State Circuits Conf. Fox, J. Hynecek, and D. Advantages and drawbacks Proc. Tu, R. Hornsey, and S. IEEE Can. Storm, R. Henderson, J. Hurwitz, D. Renshaw, K. Findlater, and M. The authors would like to thank Dr. Lubzens, [23] S. Decker, D. McGrath, K. Brehmer, and C. Lineykin, A. Teman, S. Glozman, T. Blockstein for valuable discussions. Wang, S. Barna, S. Campbell, and E. Kavusi and A. Image Sens. Patent 6 B2, May 3, Yang, A. El Gamal, B.

Fowler, and H. Akahane, S. Sugawa, S. Mori, T. Ishiuchi, and image sensor with ultra wide dynamic range floating point pixel level K. Rhee and Y. Woonghee, N. Guilvard, J.

CMOS Imagers: From Phototransduction to Image Processing (Fundamental Theories of Physics)

Segura, P. Magnan, and P. Hertel, A. Betts, R. Hicks, and M. Egawa, H. Koike, R. Okamoto, H. Yamashita, N. Tanaka, J. IEEE Intell. Arakawa, H. Ishida, H. Harakawa, T. Sakai, and H. SSC, no. Yoshitaka, T. Nagataka, K. Nobuhiro, S. Hiromichi, N. Akira, [30] X. Wang, W. Wong, and R. Hiroto, L.


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Yoshinori, and M. Solid- Devices, vol. State Circuits Conf. Yusuke, T. Atushi, T. Tadayuki, K. Akihiko, S. Hiroki, K. Solid-State Circuits, and N. Culurciello, R. Etienne-Cummings, and K. Workshop, Jun. Yadid-Pecht and A. IEEE J. Brajovic and T. Hamamoto and K. Kitchen, A. Bermak, and A. Acosta-Serafini, I. Masaki, and C. Bermak and A. Fish, A. Circuits Syst. II, Exp. Bermak and Y. Very Large Scale Integr. Belenky, A. Spivak, and O. Circuits [37] L. Qiang, J. Harris, and Z. Briefs, vol. Circuits Signal [59] S. Han, S. Kim, J. Choi, C. Kim, and E. Shoushun and A. Very Large Tech. Papers, , pp. Scale Integr.

VLSI Syst. Stoppa, A. Vatteroni, D. Covi, A. Automatic segmentation of tissues is, however, a difficult task. The aim of this work was to develop and evaluate an algorithm that automatically segments tissues in CT images of the male pelvis.

Heart cells’ environment a potentially major factor in heart disease

The newly developed algorithm MK combines histogram matching, thresholding, region growing, deformable model and atlas-based registration techniques for the segmentation of bones, adipose tissue, prostate and muscles in CT images. Visual inspection of segmented images showed that the algorithm performed well for the five analysed images.

The tissues were identified and outlined with accuracy sufficient for the dual-energy iterative reconstruction algorithm whose aim is to improve the accuracy of radiation treatment planning in brachytherapy of the prostate. New paradigms for parallel programming have been devised to simplify software development on multi-core processors and many-core graphical processing units GPU. Despite their obvious benefits, the parallelisation of existing computer programs is not an easy task.

Parallelisation of the code with the OpenMP was easy and resulted in an overall speedup of 15 on a core computer. Parallelisation with OpenCL was more difficult owing to differences between the central processing unit and GPU architectures.

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The resulting speedup was substantially lower than the theoretical peak performance of the GPU; the cause was explained. Recent years have shown great progress in driving assistance systems, approaching autonomous driving step by step. Many approaches rely on lane markers however, which limits the system to larger paved roads and poses problems during winter. In this work we explore an alternative approach to visual road following based on online learning. The system learns the current visual appearance of the road while the vehicle is operated by a human.

When driving onto a new type of road, the human driver will drive for a minute while the system learns. After training, the human driver can let go of the controls. The present work proposes a novel approach to online perception-action learning for the specific problem of road following, which makes interchangeably use of supervised learning by demonstration , instantaneous reinforcement learning, and unsupervised learning self-reinforcement learning. The proposed method, symbiotic online learning of associations and regression SOLAR , extends previous work on qHebb-learning in three ways: priors are introduced to enforce mode selection and to drive learning towards particular goals, the qHebb-learning methods is complemented with a reinforcement variant, and a self-assessment method based on predictive coding is proposed.

The system demonstrates an ability to learn to follow paved and gravel roads outdoors. Further, the system is evaluated in a controlled indoor environment which provides quantifiable results. The experiments show that the SOLAR algorithm results in autonomous capabilities that go beyond those of existing methods with respect to speed, accuracy, and functionality. It was the first benchmark on short-term,single-target tracking in thermal infrared TIR sequences.

The challenge aimed at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Driver assistance systems in modern cars now show clear steps towards autonomous driving and improvements are presented in a steady pace. The total number of sensors has also decreased from the vehicles of the initial DARPA challenge, more resembling a pile of sensors with a car underneath.

Still, anyone driving a tele-operated toy using a video link is a demonstration that a single camera provides enough information about the surronding world. Most lane assist systems are developed for highway use and depend on visible lane markers. However, lane markers may not be visible due to snow or wear, and there are roads without lane markers. With a slightly different approach, autonomous road following can be obtained on almost any kind of road.

Using realtime online machine learning, a human driver can demonstrate driving on a road type unknown to the system and after some training, the system can seamlessly take over. The demonstrator system presented in this work has shown capability of learning to follow different types of roads as well as learning to follow a person. The system is based solely on vision, mapping camera images directly to control signals. Such systems need the ability to handle multiple-hypothesis outputs as there may be several plausible options in similar situations. If there is an obstacle in the middle of the road, the obstacle can be avoided by going on either side.

However the average action, going straight ahead, is not a viable option. Similarly, at an intersection, the system should follow one road, not the average of all roads. To this end, an online machine learning framework is presented where inputs and outputs are represented using the channel representation. The learning system is structurally simple and computationally light, based on neuropsychological ideas presented by Donald Hebb over 60 years ago. Nonetheless the system has shown a cabability to learn advanced tasks. Furthermore, the structure of the system permits a statistical interpretation where a non-parametric representation of the joint distribution of input and output is generated.

Prediction generates the conditional distribution of the output, given the input. The statistical interpretation motivates the introduction of priors. In cases with multiple options, such as at intersections, a prior can select one mode in the multimodal distribution of possible actions. In addition to the ability to learn from demonstration, a possibility for immediate reinforcement feedback is presented.

This allows for a system where the teacher can choose the most appropriate way of training the system, at any time and at her own discretion. The theoretical contributions include a deeper analysis of the channel representation. A geometrical analysis illustrates the cause of decoding bias commonly present in neurologically inspired representations, and measures to counteract it. Confidence values are analyzed and interpreted as evidence and coherence.

Further, the use of the truncated cosine basis function is motivated. Finally, a selection of applications is presented, such as autonomous road following by online learning and head pose estimation. A method founded on the same basic principles is used for visual tracking, where the probabilistic representation of target pixel values allows for changes in target appearance. Thermal cameras have historically been of interest mainly for military applications. Increasing image quality and resolution combined with decreasing price and size during recent years have, however, opened up new application areas.

They are now widely used for civilian applications, e. Thermal cameras are useful as soon as it is possible to measure a temperature difference. Compared to cameras operating in the visual spectrum, they are advantageous due to their ability to see in total darkness, robustness to illumination variations, and less intrusion on privacy. This thesis addresses the problem of detection and tracking in thermal infrared imagery.

Visual detection and tracking of objects in video are research areas that have been and currently are subject to extensive research. Benchmark results indicate that detection and tracking are still challenging problems. A common belief is that detection and tracking in thermal infrared imagery is identical to detection and tracking in grayscale visual imagery. This thesis argues that the preceding allegation is not true.

The characteristics of thermal infrared radiation and imagery pose certain challenges to image analysis algorithms. The thesis describes these characteristics and challenges as well as presents evaluation results confirming the hypothesis. Detection and tracking are often treated as two separate problems. However, some tracking methods, e. They learn a model of the object that is adaptively updated. That is, detection and tracking are performed jointly. The thesis includes a template-based tracking method designed specifically for thermal infrared imagery, describes a thermal infrared dataset for evaluation of template-based tracking methods, and provides an overview of the first challenge on short-term,single-object tracking in thermal infrared video.

Finally, two applications employing detection and tracking methods are presented. This paper presents a study on a family of local hexagonal and multi-scale operators useful for texture analysis. The hexagonal grid shows an attractive rotation symmetry with uniform neighbour distances. The operator depicts a closed connected curve 1D periodic. It is resized within a scale interval during the conversion from the original square grid to the virtual hexagonal grid. Complementary image features, together with their tangential first-order hexagonal derivatives, are calculated.

Similarity metrics are used as template matching. The sample, unseen by the system, is classified into the group with the maximum fuzzy rank order. A similar evaluation, using a box-like point mask of square grids, gives overall lower accuracies. Finally, the FrFT parameter is an additional tuning parameter influencing the accuracies significantly. Visual object tracking performance has improved significantly in recent years. Most trackers are based on either of two paradigms: online learning of an appearance model or the use of a pre-trained object detector. Methods based on online learning provide high accuracy, but are prone to model drift.

Methods based on a detector on the other hand typically have good long-term robustness, but reduced accuracy compared to online methods. Despite the complementarity of the aforementioned approaches, the problem of fusing them into a single framework is largely unexplored. In this paper, we propose a novel fusion between an online tracker and a pre-trained detector for tracking humans from a UAV.

The system operates at real-time on a UAV platform. In addition we present a novel dataset for long-term tracking in a UAV setting, that includes scenarios that are typically not well represented in standard visual tracking datasets. The fast progress has been possible thanks to the development of new template-based tracking methods with online template updates, methods which have not been explored for TIR tracking. Instead, tracking methods used for TIR are often subject to a number of constraints, e. In this paper, we propose a template-based tracking method ABCD designed specifically for TIR and not being restricted by any of the constraints above.

In order to avoid background contamination of the object template, we propose to exploit background information for the online template update and to adaptively select the object region used for tracking. Moreover, we propose a novel method for estimating object scale change. Random Forests RF is a learning techniquewith very low run-time complexity.

It has found a nicheapplication in situations where input data is low-dimensionaland computational performance is paramount. We wish tomake RFs more useful for high dimensional problems, andto this end, we propose two extensions to RFs: Firstly, afeature selection mechanism called correlation-enhancing pro-jections, and secondly sparse discriminant selection schemes forbetter accuracy and faster training.

We evaluate the proposedextensions by performing age and gender estimation on theMORPH-II dataset, and demonstrate near-equal or improvedestimation performance when using these extensions despite aseventy-fold reduction in the number of data dimensions. One of the major steps in visual environment perception for automotive applications is to track keypoints and to subsequently estimate egomotion and environment structure from the trajectories of these keypoints.

This paper presents a propagation based tracking method to obtain the 2D trajectories of keypoints from a sequence of images in a monocular camera setup. Instead of relying on the classical RANSAC to obtain accurate keypoint correspondences, we steer the search for keypoint matches by means of propagating the estimated 3D position of the keypoint into the next frame and verifying the photometric consistency.

In this process, we continuously predict, estimate and refine the frame-to-frame relative pose which induces the epipolar relation. We present a framework that supports the development and evaluation of vision algorithms in the context of driver assistance applications and traffic surveillance. This framework allows the creation of highly realistic image sequences featuring traffic scenarios. The sequences are created with a realistic state of the art vehicle physics model; different kinds of environments are featured, thus providing a wide range of testing scenarios. Due to the physically-based rendering technique and variable camera models employed for the image rendering process, we can simulate different sensor setups and provide appropriate and fully accurate ground truth data.

Correspondence relations between different views of the same scene can be learnt in an unsupervised manner. We address autonomous learning of arbitrary fixed spatial point-to-point mappings. Since any such transformation can be represented by a permutation matrix, the signal model is a linear one, whereas the proposed analysis method, mainly based on Canonical Correlation Analysis CCA is based on a generalized eigensystem problem, i. Automatic analysis of visual art, such as paintings, is a challenging inter-disciplinary research problem. Conventional approaches only rely on global scene characteristics by encoding holistic information for computational painting categorization.

We argue that such approaches are sub-optimal and that discriminative common visual structures provide complementary information for painting classification. We present an approach that encodes both the global scene layout and discriminative latent common structures for computational painting categorization. The region of interests are automatically extracted, without any manual part labeling, by training class-specific deformable part-based models. Both holistic and region-of-interests are then described using multi-scale dense convolutional features. These features are pooled separately using Fisher vector encoding and concatenated afterwards in a single image representation.

Experiments are performed on a challenging dataset with 91 different painters and 13 diverse painting styles. Our approach outperforms the standard method, which only employs the global scene characteristics. Furthermore, our method achieves state-of-the-art results outperforming a recent multi-scale deep features based approach [11] by 6.

In recent years, probabilistic registration approaches have demonstrated superior performance for many challenging applications. Generally, these probabilistic approaches rely on the spatial distribution of the 3D-points, and only recently color information has been integrated into such a framework, significantly improving registration accuracy. Other than local color information, high-dimensional 3D shape features have been successfully employed in many applications such as action recognition and 3D object recognition.

In this paper, we propose a probabilistic framework to integrate high-dimensional 3D shape features with color information for point set registration. The 3D shape features are distinctive and provide complementary information beneficial for robust registration. We validate our proposed framework by performing comprehensive experiments on the challenging Stanford Lounge dataset, acquired by a RGB-D sensor, and an outdoor dataset captured by a Lidar sensor.

The results clearly demonstrate that our approach provides superior results both in terms of robustness and accuracy compared to state-of-the-art probabilistic methods. In this paper we introduce an efficient method to unwrap multi-frequency phase estimates for time-of-flight ranging. The algorithm generates multiple depth hypotheses and uses a spatial kernel density estimate KDE to rank them.

The confidence produced by the KDE is also an effective means to detect outliers. We also introduce a new closed-form expression for phase noise prediction, that better fits real data.

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The method is applied to depth decoding for the Kinect v2 sensor, and compared to the Microsoft Kinect SDK and to the open source driver libfreenect2. The intended Kinect v2 use case is scenes with less than 8m range, and for such cases we observe consistent improvements, while maintaining real-time performance. When extending the depth range to the maximal value of The effect is that the sensor can now be used in large depth scenes, where it was previously not a good choice.

Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR challenge is similar to the challenge, the main difference is the introduction of new, more difficult sequences into the dataset. Compared to VOT-TIR, a significant general improvement of results has been observed, which partly compensate for the more difficult sequences.

The dataset, the evaluation kit, as well as the results are publicly available at the challenge website. Robust visual tracking is a challenging computer vision problem, with many real-world applications. Recently, deep RGB features extracted from convolutional neural networks have been successfully applied for tracking.

Despite their success, these features only capture appearance information. On the other hand, motion cues provide discriminative and complementary information that can improve tracking performance. This paper presents an investigation of the impact of deep motion features in a tracking-by-detection framework. We further show that hand-crafted, deep RGB, and deep motion features contain complementary information.

Comprehensive experiments clearly suggest that our fusion approach with deep motion features outperforms standard methods relying on appearance information alone. The Visual Object Tracking challenge VOT aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 70 trackers are presented, with a large number of trackers being published at major computer vision conferences and journals in the recent years. The number of tested state-of-the-art trackers makes the VOT the largest and most challenging benchmark on short-term tracking to date.

For each participating tracker, a short description is provided in the Appendix. The VOT goes beyond its predecessors by i introducing a new semi-automatic ground truth bounding box annotation methodology and ii extending the evaluation system with the no-reset experiment. We address two problems related to large-scale aerial monitoring of district heating networks. First, we propose a classification scheme to reduce the number of false alarms among automatically detected leakages in district heating networks.

The leakages are detected in images captured by an airborne thermal camera, and each detection corresponds to an image region with abnormally high temperature. This approach yields a significant number of false positives, and we propose to reduce this number in two steps; by a using a building segmentation scheme in order to remove detections on buildings, and b to use a machine learning approach to classify the remaining detections as true or false leakages.

We provide extensive experimental analysis on real-world data, showing that this post-processing step significantly improves the usefulness of the system. Second, we propose a method for characterization of leakages over time, i. We address the problem of finding trends in the degradation of pipe networks in order to plan for long-term maintenance, and propose a visualization scheme exploiting the consecutive data collections. Attitude pitch and roll angle estimation from visual information is necessary for GPS-free navigation of airborne vehicles.

We propose a highly accurate method to estimate the attitude by horizon detection in fisheye images. A Canny edge detector and a probabilistic Hough voting scheme are used to compute an approximate attitude and the corresponding horizon line in the image. Horizon edge pixels are extracted in a band close to the approximate horizon line. The attitude estimates are refined through registration of the extracted edge pixels with the geometrical horizon from a digital elevation map DEM , in our case the SRTM3 database, extracted at a given approximate position. To achieve the high-accuracy attitude estimates, the ray refraction in the earth's atmosphere has been taken into account.

The attitude errors obtained on real images are less or equal to those achieved on synthetic images for previous methods with DEM refinement, and the errors are about one order of magnitude smaller than for any previous vision-based method without DEM refinement. Discriminative Correlation Filters DCF have demonstrated excellent performance for visual object tracking. The key to their success is the ability to efficiently exploit available negative data by including all shifted versions of a training sample. However, the underlying DCF formulation is restricted to single-resolution feature maps, significantly limiting its potential.

In this paper, we go beyond the conventional DCF framework and introduce a novel formulation for training continuous convolution filters. We employ an implicit interpolation model to pose the learning problem in the continuous spatial domain. Additionally, our approach is capable of sub-pixel localization, crucial for the task of accurate feature point tracking. We also demonstrate the effectiveness of our learning formulation in extensive feature point tracking experiments.

In recent years, sensors capable of measuring both color and depth information have become increasingly popular. Despite the abundance of colored point set data, state-of-the-art probabilistic registration techniques ignore the available color information. In this paper, we propose a probabilistic point set registration framework that exploits available color information associated with the points. Our method is based on a model of the joint distribution of 3D-point observations and their color information. The proposed model captures discriminative color information, while being computationally efficient.

We derive an EM algorithm for jointly estimating the model parameters and the relative transformations. Comprehensive experiments are performed on the Stanford Lounge dataset, captured by an RGB-D camera, and two point sets captured by a Lidar sensor. Our results demonstrate a significant gain in robustness and accuracy when incorporating color information. Furthermore, our proposed model outperforms standard strategies for combining color and 3D-point information, leading to state-of-the-art results.

Tracking-by-detection methods have demonstrated competitive performance in recent years. In these approaches, the tracking model heavily relies on the quality of the training set. Due to the limited amount of labeled training data, additional samples need to be extracted and labeled by the tracker itself. This often leads to the inclusion of corrupted training samples, due to occlusions, misalignments and other perturbations. Existing tracking-by-detection methods either ignore this problem, or employ a separate component for managing the training set.

We propose a novel generic approach for alleviating the problem of corrupted training samples in tracking-by-detection frameworks. Our approach dynamically manages the training set by estimating the quality of the samples. Contrary to existing approaches, we propose a unified formulation by minimizing a single loss over both the target appearance model and the sample quality weights. The joint formulation enables corrupted samples to be down-weighted while increasing the impact of correct ones.

On the OTB, our unified formulation significantly improves the baseline, with a gain of 3. Finally, our method achieves state-of-the-art results on all three datasets.

The Science of Camera Sensors

In this article we provide an overview of color name applications in computer vision. Color names are linguistic labels which humans use to communicate color. Computational color naming learns a mapping from pixels values to color names. In recent years color names have been applied to a wide variety of computer vision applications, including image classification, object recognition, texture classification, visual tracking and action recognition.

Here we provide an overview of these results which show that in general color names outperform photometric invariants as a color representation. Although the topic has a long history in the image processing community, researchers continuously present novel methods to obtain ever better image restoration results. With an expanding market for individuals who wish to share their everyday life on social media, imaging techniques such as compact cameras and smart phones are important factors.

Naturally, every producer of imaging equipment desires to exploit cheap camera components while supplying high quality images.


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  6. One step in this pipeline is to use sophisticated imaging software including, e. This thesis is based on traditional formulations such as isotropic and tensor-based anisotropic diffusion for image denoising. The difference from main-stream denoising methods is that this thesis explores the effects of introducing contextual information as prior knowledge for image denoising into the filtering schemes. To achieve this, the adaptive filtering theory is formulated from an energy minimization standpoint. The core contributions of this work is the introduction of a novel tensor-based functional which unifies and generalises standard diffusion methods.

    Additionally, the explicit Euler-Lagrange equation is derived which, if solved, yield the stationary point for the minimization problem. Several aspects of the functional are presented in detail which include, but are not limited to, tensor symmetry constraints and convexity. Also, the classical problem of finding a variational formulation to a given tensor-based partial differential equation is studied. The presented framework is applied in problem formulation that includes non-linear domain transformation, e.

    Additionally, the framework is also used to exploit locally estimated probability density functions or the channel representation to drive the filtering process. Furthermore, one of the first truly tensor-based formulations of total variation is presented. The key to the formulation is the gradient energy tensor, which does not require spatial regularization of its tensor components.

    It is shown empirically in several computer vision applications, such as corner detection and optical flow, that the gradient energy tensor is a viable replacement for the commonly used structure tensor. Moreover, the gradient energy tensor is used in the traditional tensor-based anisotropic diffusion scheme. This approach results in significant improvements in computational speed when the scheme is implemented on a graphical processing unit compared to using the commonly used structure tensor.

    Many image processing methods such as corner detection,optical flow and iterative enhancement make use of image tensors. Generally, these tensors are estimated using the structure tensor. In this work we show that the gradient energy tensor can be used as an alternativeto the structure tensor in several cases. We apply the gradient energy tensor to common image problem applications such as corner detection, optical flow and image enhancement. Reliable detection of obstacles at long range is crucial for the timely response to hazards by fast-moving safety-critical platforms like autonomous cars.

    We present a novel method for the joint detection and localization of distant obstacles using a stereo vision system on a moving platform. The approach is applicable to both static and moving obstacles and pushes the limits of detection performance as well as localization accuracy. The proposed detection algorithm is based on sound statistical tests using local geometric criteria which implicitly consider non-flat ground surfaces.

    To achieve maximum performance, it operates directly on image data instead of precomputed stereo disparity maps. A careful experimental evaluation on several datasets shows excellent detection performance and localization accuracy up to very large distances, even for small obstacles. We demonstrate a parallel implementation of the proposed system on a GPU that executes at real-time speeds.

    Action recognition in still images is a challenging problem in computer vision. To facilitate comparative evaluation independently of person detection, the standard evaluation protocol for action recognition uses an oracle person detector to obtain perfect bounding box information at both training and test time. The assumption is that, in practice, a general person detector will provide candidate bounding boxes for action recognition. In this paper, we argue that this paradigm is suboptimal and that action class labels should already be considered during the detection stage.

    Motivated by the observation that body pose is strongly conditioned on action class, we show that: 1 the existing state-of-the-art generic person detectors are not adequate for proposing candidate bounding boxes for action classification; 2 due to limited training examples, the direct training of action-specific person detectors is also inadequate; and 3 using only a small number of labeled action examples, the transfer learning is able to adapt an existing detector to propose higher quality bounding boxes for subsequent action classification. To the best of our knowledge, we are the first to investigate transfer learning for the task of action-specific person detection in still images.

    For the action detection task i. Our approach also significantly outperforms the state of the art with a MAP of We also evaluate our action detection approach for the task of action classification i. For this task, our approach, without using any ground-truth person localization at test time, outperforms on both data sets state-of-the-art methods, which do use person locations. In this study, we investigate the backward p x -parabolic equation as a new methodology to enhance images.

    We propose a novel iterative regularization procedure for the backward p x -parabolic equation based on the nonlinear Landweber method for inverse problems. The proposed scheme can also be extended to the family of iterative regularization methods involving the nonlinear Landweber method. We also investigate the connection between the variable exponent p x in the proposed energy functional and the diffusivity function in the corresponding Euler-Lagrange equation.

    It is well known that the forward problems converges to a constant solution destroying the image. The purpose of the approach of the backward problems is twofold. First, solving the backward problem by a sequence of forward problems we obtain a smooth image which is denoised. Second, by choosing the initial data properly we try to reduce the blurriness of the image.

    The numerical results for denoising appear to give improvement over standard methods as shown by preliminary results. Visual odometry is one of the most active topics in computer vision. The automotive industry is particularly interested in this field due to the appeal of achieving a high degree of accuracy with inexpensive sensors such as cameras. The best results on this task are currently achieved by systems based on a calibrated stereo camera rig, whereas monocular systems are generally lagging behind in terms of performance.

    We hypothesise that this is due to stereo visual odometry being an inherently easier problem, rather than than due to higher quality of the state of the art stereo based algorithms. Under this hypothesis, techniques developed for monocular visual odometry systems would be, in general, more refined and robust since they have to deal with an intrinsically more difficult problem. In this work we present a novel stereo visual odometry system for automotive applications based on advanced monocular techniques.

    We show that the generalization of these techniques to the stereo case result in a significant improvement of the robustness and accuracy of stereo based visual odometry. Graphic Novels Comic Strips. My Wishlist. Know about stores. Products of this store will be shipped directly from the US to your country. Products of this store will be shipped directly from the UK to your country. Products of this store will be shipped directly from China to your country.

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