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

http://cvssp-data.eps.surrey.ac.uk/Personal/AdrianHilton/Publications_files/survey_techrep.pdf

A survey of advances in vision-based human motion capture and analysis

The development of the Internet in recent years has made it possible and useful to access many different information systems anywhere in the world to obtain information. While there is much research on the integration of heterogeneous information systems, most commercial systems stop short of the actual integration of available data. Data fusion is the process of fusing multiple records representing the same real-world object into a single, consistent, and clean representation.This article places data fusion into the greater context of data integration, precisely defines the goals of data fusion, namely, complete, concise, and consistent data, and highlights the challenges of data fusion, namely, uncertain and conflicting data values. We give an overview and classification of different ways of fusing data and present several techniques based on standard and advanced operators of the relational algebra and SQL. Finally, the article features a comprehensive survey of data integration systems from academia and industry, showing if and how data fusion is performed in each.

Data fusion

Salient object detection has been attracting a lot of interest, and recently various heuristic computational models have been designed. In this paper, we regard saliency map computation as a regression problem. Our method, which is based on multi-level image segmentation, uses the supervised learning approach to map the regional feature vector to a saliency score, and finally fuses the saliency scores across multiple levels, yielding the saliency map. The contributions lie in two-fold. One is that we show our approach, which integrates the regional contrast, regional property and regional background ness descriptors together to form the master saliency map, is able to produce superior saliency maps to existing algorithms most of which combine saliency maps heuristically computed from different types of features. The other is that we introduce a new regional feature vector, background ness, to characterize the background, which can be regarded as a counterpart of the objectness descriptor [2]. The performance evaluation on several popular benchmark data sets validates that our approach outperforms existing state-of-the-arts.

https://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Jiang_Salient_Object_Detection_2013_CVPR_paper.pdf

Salient Object Detection: A Discriminative Regional Feature Integration Approach

Anomaly detection in videos refers to the identification of events that do not conform to expected behavior. However, almost all existing methods tackle the problem by minimizing the reconstruction errors of training data, which cannot guarantee a larger reconstruction error for an abnormal event. In this paper, we propose to tackle the anomaly detection problem within a video prediction framework. To the best of our knowledge, this is the first work that leverages the difference between a predicted future frame and its ground truth to detect an abnormal event. To predict a future frame with higher quality for normal events, other than the commonly used appearance (spatial) constraints on intensity and gradient, we also introduce a motion (temporal) constraint in video prediction by enforcing the optical flow between predicted frames and ground truth frames to be consistent, and this is the first work that introduces a temporal constraint into the video prediction task. Such spatial and motion constraints facilitate the future frame prediction for normal events, and consequently facilitate to identify those abnormal events that do not conform the expectation. Extensive experiments on both a toy dataset and some publicly available datasets validate the effectiveness of our method in terms of robustness to the uncertainty in normal events and the sensitivity to abnormal events. All codes are released in https://github.com/StevenLiuWen/ano_pred_cvpr2018.

/pdf/future-frame-prediction-for-anomaly-detection-a-new-baseline-4n3l3cjdjp.pdf

Future Frame Prediction for Anomaly Detection - A New Baseline

The wearable tactile navigation system frees you from requiring to use your eyes as there is no display, all positional information is conveyed via touch. As a compass, the device nudges you towards North. As a GPS navigator, the device orients you towards a landmark (i.e., home) and lets you feel how far away home is. A bluetooth interface provides network capabilities, allowing you to download map landmarks from a cell phone. The bidirectional networking capability generalizes the device to a platform capable of collecting any sensor data as well as providing tactile messages and touch telepresence. The main application of the device is a wayfinding device for people that are blind and for people that suffer from Alzheimer's disease but there are many other applications where it is desirable to provide geographical information in tactile form as opposed to providing it in visual or auditory form.

Wearable tactile navigation system

Keyframe-based monocular SLAM

A novel statistical textural distinctiveness approach for robustly detecting salient regions in natural images is proposed. Rotational-invariant neighborhood-based textural representations are extracted and used to learn a set of representative texture atoms for defining a sparse texture model for the image. Based on the learnt sparse texture model, a weighted graphical model is constructed to characterize the statistical textural distinctiveness between all representative texture atom pairs. Finally, the saliency of each pixel in the image is computed based on the probability of occurrence of the representative texture atoms, their respective statistical textural distinctiveness based on the constructed graphical model, and general visual attentive constraints. Experimental results using a public natural image dataset and a variety of performance evaluation metrics show that the proposed approach provides interesting and promising results when compared to existing saliency detection methods.

/pdf/statistical-textural-distinctiveness-for-salient-region-59d2el25i5.pdf

Statistical Textural Distinctiveness for Salient Region Detection in Natural Images

http://www.eng.uwaterloo.ca/~dclausi/Theses/Tung_FrederickMASc2010.pdf

Goal-based trajectory analysis for unusual behaviour detection in intelligent surveillance

The Canada Centre for Remote Sensing has developed two hierarchical expert systems, the Analyst Advisor and the Map Image Congruency Evaluation (MICE) advisor. These expert systems are built upon our Remote-Sensing Shell (RESHELL) written in Logicware's MPROLOG. A shell is a programming environment that specifically caters to expert system development. Knowledge is represented in the production rules and frames database. Numerical processing takes place using the extensive FORTRAN code of the Landsat Digital Image Analysis System (LDIAS). The LDIAS includes several DEC VAX computers, image displays, specialized processors, and DEC Al VAXstations. The paper describes the architecture of the expert system to compare maps and images (MICE) and the expert system to advise on the extraction of resource information from remotely sensed data, the Analyst Advisor. Details are given concerning the structure of RESHELL and our methods of interfacing symbolic reasoning in PROLOG on the Al VAX stations with numeric processing in FORTRAN on several different computers. The first prototype of the Analyst Advisor will be released for internal use at CCRS in March 1987.

John Zelek

Papers

Wearable tactile navigation system

Keyframe-based monocular SLAM

Statistical Textural Distinctiveness for Salient Region Detection in Natural Images

Goal-based trajectory analysis for unusual behaviour detection in intelligent surveillance

An Expert System for Remote Sensing