From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Tables of integrals

Current top-performing object detectors depend on deep CNN backbones, such as ResNet-101 and Inception, benefiting from their powerful feature representations but suffering from high computational costs. Conversely, some lightweight model based detectors fulfil real time processing, while their accuracies are often criticized. In this paper, we explore an alternative to build a fast and accurate detector by strengthening lightweight features using a hand-crafted mechanism. Inspired by the structure of Receptive Fields (RFs) in human visual systems, we propose a novel RF Block (RFB) module, which takes the relationship between the size and eccentricity of RFs into account, to enhance the feature discriminability and robustness. We further assemble RFB to the top of SSD, constructing the RFB Net detector. To evaluate its effectiveness, experiments are conducted on two major benchmarks and the results show that RFB Net is able to reach the performance of advanced very deep detectors while keeping the real-time speed. Code is available at https://github.com/ruinmessi/RFBNet.

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Receptive Field Block Net for Accurate and Fast Object Detection

A survey of recent advances in visual feature detection

Supply chain network design (SCND) is one of the important, primary and strategic decisions affecting competitive advantages and all other decisions in supply chain management. Although most of papers in SCND focus only on the economic performance, this study considers simultaneously economic, social and environmental aspects. In this study, a new mixed integer nonlinear programming model is developed to formulate a multi-objective sustainable closed-loop supply chain network design problem by considering discount supposition in the transportation costs for the first time. In order to address the problem, not only traditional and recent metaheuristics are utilized, but also the algorithms are hybridized according to their strengths especially in intensification and diversification. To evaluate the efficiency and effectiveness of these algorithms, they are compared with each other by four assessment metrics for Pareto optimal analyses. Although the results indicate the performance of three proposed new hybridization algorithms, KAGA achieves better solutions compared with the others, but it needs more time. At the end, we introduced a real industrial example in glass industry to verify the proposed model and the algorithms.

Sustainable closed-loop supply chain network design with discount supposition

Contour detection model with multi-scale integration based on non-classical receptive field

A novel method for 2D nonrigid partial shape matching

Shape representation is an extremely important and longstanding problem in the field of pattern recognition. Closed contour, which refers to shape contour, plays a crucial role in the comparison of shapes. Because shape contour is the most stable, distinguishable, and invariable feature of an object, it is useful to incorporate it into the recognition process. This paper proposes a method based on genetic algorithms. The proposed method can be used to identify the most common contour fragments, which can be used to represent the contours of a shape category. The common fragments clarify the particular logics included in the contours. This paper shows that the explicit representation of the shape contour contributes significantly to shape representation and object recognition.

A Genetic-Algorithm-Based Explicit Description of Object Contour and its Ability to Facilitate Recognition

Helping robots understand indoor scenes has considerable value in computer vision. However, due to the diversity of indoor scenes, understanding them remains a big challenge. There are many spatial right-angles in indoor scenes. These spatial right-angles are projected into diverse 2D projections. These projections can be considered a composition of a pair of lines (line-pairs). Given the vanishing points (VPs), line segments can be assigned to 1 of 3 main orthogonal directions. The line-pairs (intersection of 2 lines), such that each of them converges to a different VP, are likely to be the projection of a spatial right-angle onto the image plane. These projections may enable us to estimate their original orientation and position in 3D scenes. In this paper, we presented a method to efficiently understand indoor scenes from a single image, without training or any knowledge of the camera’s internal calibration. Through geometric inference of line-pairs, it is possible to find these spatial right-angle projections. Then, these projections can be assigned to different clusters, and the line that lies in the neighbor-cluster helps us estimate the layout of the indoor scene. The proposed approach required no prior training. We compared the room layout estimated by our algorithm against the room box ground truth, measuring the percentage of pixels that were correctly classified. These experiments showed that our method estimated not only room layout, but also details of the indoor scene.

Visual Navigation Using Projection of Spatial Right-Angle In Indoor Environment

Studies in neuroscience and biological vision have shown that the human retina has strong computational power, and its information representation supports vision tasks on both ventral and dorsal pathways. In this paper, a new local image descriptor, termed distinctive efficient robust features (DERF), is derived by modeling the response and distribution properties of the parvocellular-projecting ganglion cells in the primate retina. DERF features exponential scale distribution, exponential grid structure, and circularly symmetric function difference of Gaussian (DoG) used as a convolution kernel, all of which are consistent with the characteristics of the ganglion cell array found in neurophysiology, anatomy, and biophysics. In addition, a new explanation for local descriptor design is presented from the perspective of wavelet tight frames. DoG is naturally a wavelet, and the structure of the grid points array in our descriptor is closely related to the spatial sampling of wavelets. The DoG wavelet itself forms a frame, and when we modulate the parameters of our descriptor to make the frame tighter, the performance of the DERF descriptor improves accordingly. This is verified by designing a tight frame DoG, which leads to much better performance. Extensive experiments conducted in the image matching task on the multiview stereo correspondence data set demonstrate that DERF outperforms state of the art methods for both hand-crafted and learned descriptors, while remaining robust and being much faster to compute.

Hui Wei

Papers

Contour detection model with multi-scale integration based on non-classical receptive field

A novel method for 2D nonrigid partial shape matching

A Genetic-Algorithm-Based Explicit Description of Object Contour and its Ability to Facilitate Recognition

Visual Navigation Using Projection of Spatial Right-Angle In Indoor Environment

DERF: Distinctive Efficient Robust Features From the Biological Modeling of the P Ganglion Cells