Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

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

The PASCAL Visual Object Classes Challenge

We develop a new edge detection algorithm that addresses two critical issues in this long-standing vision problem: (1) holistic image training, and (2) multi-scale feature learning. Our proposed method, holistically-nested edge detection (HED), turns pixel-wise edge classification into image-to-image prediction by means of a deep learning model that leverages fully convolutional neural networks and deeply-supervised nets. HED automatically learns rich hierarchical representations (guided by deep supervision on side responses) that are crucially important in order to approach the human ability to resolve the challenging ambiguity in edge and object boundary detection. We significantly advance the state-of-the-art on the BSD500 dataset (ODS F-score of 0.782) and the NYU Depth dataset (ODS F-score of 0.746), and do so with an improved speed (0.4 second per image) that is orders of magnitude faster than recent CNN-based edge detection algorithms.

https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Xie_Holistically-Nested_Edge_Detection_ICCV_2015_paper.pdf

Holistically-Nested Edge Detection

Anomaly detection is a classical problem in computer vision, namely the determination of the normal from the abnormal when datasets are highly biased towards one class (normal) due to the insufficient sample size of the other class (abnormal). While this can be addressed as a supervised learning problem, a significantly more challenging problem is that of detecting the unknown/unseen anomaly case that takes us instead into the space of a one-class, semi-supervised learning paradigm. We introduce such a novel anomaly detection model, by using a conditional generative adversarial network that jointly learns the generation of high-dimensional image space and the inference of latent space. Employing encoder-decoder-encoder sub-networks in the generator network enables the model to map the input image to a lower dimension vector, which is then used to reconstruct the generated output image. The use of the additional encoder network maps this generated image to its latent representation. Minimizing the distance between these images and the latent vectors during training aids in learning the data distribution for the normal samples. As a result, a larger distance metric from this learned data distribution at inference time is indicative of an outlier from that distribution—an anomaly. Experimentation over several benchmark datasets, from varying domains, shows the model efficacy and superiority over previous state-of-the-art approaches.

/pdf/ganomaly-semi-supervised-anomaly-detection-via-adversarial-2kjzyn0ubx.pdf

GANomaly : semi-supervised anomaly detection via adversarial training.

Abnormal behavior detection in crowded scenes is extremely challenging in the field of computer vision due to severe inter-object occlusions, varying crowd densities and the complex mechanics of a human crowd. We propose a method for detecting and locating anomalous activities in video sequences of crowded scenes. The key novelty of our method is the coupling of anomaly detection with a spatial-temporal Convolutional Neural Networks (CNN), which to the best of our knowledge has not been previously done. This architecture allows us to capture features from both spatial and temporal dimensions by performing spatial-temporal convolutions, thereby, both the appearance and motion information encoded in continuous frames are extracted. The spatial-temporal convolutions are only performed within spatial-temporal volumes of moving pixels to ensure robustness to local noise, and increase detection accuracy. We experimentally evaluate our model on benchmark datasets containing various situations with human crowds, and the results demonstrate that the proposed approach surpass state-of-the-art methods. HighlightsA mechanism for localizing dynamic regions in crowded scenes is proposed.A spatial-temporal Convolutional Neural Network is designed to automatically extract spatial-temporal features of the crowd.The performance of anomaly detection is improved when the analysis is concentrated on the dynamic regions only.The anomaly events that take place in small regions are effectively detected and localized by the spatial-temporal Convolutional Neural Network.

Spatial-temporal convolutional neural networks for anomaly detection and localization in crowded scenes

Object skeleton is a useful cue for object detection, complementary to the object contour, as it provides a structural representation to describe the relationship among object parts. While object skeleton extraction in natural images is a very challenging problem, as it requires the extractor to be able to capture both local and global image context to determine the intrinsic scale of each skeleton pixel. Existing methods rely on per-pixel based multi-scale feature computation, which results in difficult modeling and high time consumption. In this paper, we present a fully convolutional network with multiple scale-associated side outputs to address this problem. By observing the relationship between the receptive field sizes of the sequential stages in the network and the skeleton scales they can capture, we introduce a scale-associated side output to each stage. We impose supervision to different stages by guiding the scale-associated side outputs toward groundtruth skeletons of different scales. The responses of the multiple scaleassociated side outputs are then fused in a scale-specific way to localize skeleton pixels with multiple scales effectively. Our method achieves promising results on two skeleton extraction datasets, and significantly outperforms other competitors.

/pdf/object-skeleton-extraction-in-natural-images-by-fusing-scale-3zq6iutj3m.pdf

Object Skeleton Extraction in Natural Images by Fusing Scale-Associated Deep Side Outputs

Multiple instance subspace learning via partial random projection tree for local reflection symmetry in natural images

Anomaly detection in crowded scenes is a challenge task due to variation of the definitions for both abnormality and normality, the low resolution on the target, ambiguity of appearance, and severe occlusions of inter-object. In this paper, we propose a novel statistical framework to detect abnormal behaviors of the crowded scene by modeling trajectories of pedestrians. First, the trajectories are acquired by Kanade-Lucas-Tomasi Feature Tracker (KLT). Then trajectories are grouped to form representative trajectories, which characterize the underlying motion patterns of the crowd. Finally, trajectories are modeled by Multi-Observation Hidden Markov Model (MOHMM) to determine whether frames are normal or abnormal. The experiments are conducted on a well-known crowded scene dataset. Experimental results show that the proposed method can capture abnormal crowd behaviors successfully and achieves state-of-the-art performances.

Unusual event detection in crowded scenes by trajectory analysis

In recent years, significant progress has been made in detecting text in scene images. However, most of state-of-the-art approaches can not work well when encountered blurred, low-resolution and small-sized texts. We consider many connected regions as candidates, which aim to capture character regions as many as possible. In this paper, we propose a novel method, which is based on exhaustive segmentation, to detect text in scene images. Firstly, we present a parallel structure to generate character candidate regions with the exhaustive segmentation of scene image. Secondly, a well-designed two-layer filtering method is used to filter out non-character candidate regions. Finally, at text line grouping stage, the edges of the fully connected graph of the remaining character candidate regions are cut by a support vector machine classifier. We use two public datasets, namely, ICDAR 2013 dataset and the Street View Text dataset to evaluate the performance of our method. Experimental results show that our method achieves excellent recall rate on these two public datasets, moreover, our method is robust to the blurred, low-resolution and small-sized texts. HighlightsA parallel structure to generate character candidate regions with the exhaustive segmentation of scene images is presented.A well-designed two-layer filtering method is used to filter out non-character candidate regions.We argue that the text line grouping problem can be posed as the edge cut of the fully connected graph.The proposed method is robust to the blurred, low-resolution and small-sized texts.

Zhijiang Zhang

Papers

Spatial-temporal convolutional neural networks for anomaly detection and localization in crowded scenes

Object Skeleton Extraction in Natural Images by Fusing Scale-Associated Deep Side Outputs

Multiple instance subspace learning via partial random projection tree for local reflection symmetry in natural images

Unusual event detection in crowded scenes by trajectory analysis

Text detection in scene images based on exhaustive segmentation