Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Convolutional neural networks (CNNs) have been widely used in computer vision community, significantly improving the state-of-the-art. In most of the available CNNs, the softmax loss function is used as the supervision signal to train the deep model. In order to enhance the discriminative power of the deeply learned features, this paper proposes a new supervision signal, called center loss, for face recognition task. Specifically, the center loss simultaneously learns a center for deep features of each class and penalizes the distances between the deep features and their corresponding class centers. More importantly, we prove that the proposed center loss function is trainable and easy to optimize in the CNNs. With the joint supervision of softmax loss and center loss, we can train a robust CNNs to obtain the deep features with the two key learning objectives, inter-class dispension and intra-class compactness as much as possible, which are very essential to face recognition. It is encouraging to see that our CNNs (with such joint supervision) achieve the state-of-the-art accuracy on several important face recognition benchmarks, Labeled Faces in the Wild (LFW), YouTube Faces (YTF), and MegaFace Challenge. Especially, our new approach achieves the best results on MegaFace (the largest public domain face benchmark) under the protocol of small training set (contains under 500000 images and under 20000 persons), significantly improving the previous results and setting new state-of-the-art for both face recognition and face verification tasks.

/pdf/a-discriminative-feature-learning-approach-for-deep-face-2zrw7nm451.pdf

A Discriminative Feature Learning Approach for Deep Face Recognition

The PASCAL Visual Object Classes Challenge

This paper addresses deep face recognition (FR) problem under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space. However, few existing algorithms can effectively achieve this criterion. To this end, we propose the angular softmax (A-Softmax) loss that enables convolutional neural networks (CNNs) to learn angularly discriminative features. Geometrically, A-Softmax loss can be viewed as imposing discriminative constraints on a hypersphere manifold, which intrinsically matches the prior that faces also lie on a manifold. Moreover, the size of angular margin can be quantitatively adjusted by a parameter m. We further derive specific m to approximate the ideal feature criterion. Extensive analysis and experiments on Labeled Face in the Wild (LFW), Youtube Faces (YTF) and MegaFace Challenge 1 show the superiority of A-Softmax loss in FR tasks.

/pdf/sphereface-deep-hypersphere-embedding-for-face-recognition-bof1bk9snh.pdf

SphereFace: Deep Hypersphere Embedding for Face Recognition

A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

This paper proposes an adaptive binarization method, based on the criterion of maximizing local contrast, for document image analysis. The proposed method has overcome, to a large extent, the general problems of poor quality document images, such as non-uniform illumination, undesirable shadows and random noise. It was tested against a variety of challenging images, and the experimental results are presented to show the effectiveness and superiority of the proposed method.

Adaptive binarization method for document image analysis

We propose in this paper a doubly weighted subspace learning approach for face representation and recognition. Motivated by the fact that some face samples and parts are more effectual in characterizing and recognizing faces, we construct two weighting matrices based on pairwise similarity of face samples within a same class and discriminant score of each pixel within a face sample to duly emphasize both the between-sample and within-sample features. We then incorporate these two weighting matrices into three popular subspace learning methods, namely principal component analysis, linear discriminant analysis, and nonnegative matrix factorization, to obtain the discriminative features of faces for recognition. Moreover, the proposed doubly weighted technique can be readily extended to other newly proposed subspace learning algorithms to improve their performance. Experimental results show that the proposed approach can effectively enhance the discriminant power of the extracted face features and outperform existing, nonweighted subspace learning algorithms. The performance gain is even more apparent for cases with imbalanced training samples.

A Doubly Weighted Approach for Appearance-Based Subspace Learning Methods

Large image sensors usually contain same defects. Defects are pixels with abnormal photo-responsibility. As a result they often generate outputs different from their adjacent pixel outputs and seriously degrade the visual quality of the captured images. However, it is not economically feasible to produce sensors with no defects for rendering images. A limited number of defects are usually allowed in an image sensor as long as the defective outputs can be corrected with post signal processing techniques. In this paper we present a robust sequential approach for detecting sensor defects from a sequence of images captured by the sensor. With this approach no extra non-volatile memory is required in the sensor device to store the locations of sensor defects. In addition, the detection and correction of image defective outputs can be performed efficiently in a computer host. Experimental results of this approach are reported in the paper.

A robust sequential approach for the detection of defective pixels in an image sensor

In the conventional processing chain of single-sensor digital still cameras (DSCs), the images are captured with color filter arrays (CFAs) and the CFA samples are demosaicked into a full color image before compression. To avoid additional data redundancy created by the demosaicking process, an alternative processing chain has been proposed to move the compression process before the demosaicking. Recent empirical studies have shown that the alternative chain can outperform the conventional one in terms of image quality at low compression ratios. To provide a theoretically sound basis for such conclusion, we propose analytical models for the reconstruction errors of the two processing chains. The models developed confirm the results of existing empirical studies and provide better understanding of DSC processing chains. The modeling also allows performance predictions for more advanced compression and demosaicking methods, thus providing important cues for future development in this area

Reversing Demosaicking and Compression in Color Filter Array Image Processing: Performance Analysis and Modeling

A method to remove ringing artifacts from locations near dominant edges of an image reconstructed after compression The image is decomposed into blocks small enough so that each would contain only one significant edge A significant edge is tested as to whether it is a dominant edge of the image If there is no dominant edge, the block is not processed In the remaining blocks, the exact pixels that include the dominant edges are output without filtering The direction of the dominant edges is inferred, and then the remaining pixels are filtered with a directional de-ringing filter The de-ringing filter has a main direction that is perpendicular with the direction of the edge, and thus also with an inherent direction of the ringing artifacts

Yap-Peng Tan

Papers

Adaptive binarization method for document image analysis

A Doubly Weighted Approach for Appearance-Based Subspace Learning Methods

A robust sequential approach for the detection of defective pixels in an image sensor

Reversing Demosaicking and Compression in Color Filter Array Image Processing: Performance Analysis and Modeling

Method for removing ringing artifacts from locations near dominant edges of an image reconstructed after compression