Shadow Detection using DWT with Multi-Wavelet Selection & user Configurable Variance Parameters

Moving cast shadows are a noteworthy worry in today's execution from expansive scope of numerous vision-based observation applications in light of the fact that they exceedingly troublesome the item characterization assignment. A few shadow identification strategies have been accounted for in the writing amid the most recent years. They are for the most part partitioned into two spaces. One more often than not works with static pictures, though the second one uses picture arrangements, to be specific video content. Regardless of the way that both cases can be similarly dissected, there is a distinction in the application field. The main case, shadow identification strategies can be misused to get extra geometric and semantic signs about shape and position of its throwing article ('shape from shadows') and the restriction of the light source. While in the second one, the primary reason for existing is normally change discovery, scene coordinating or reconnaissance (for the most part in a foundation subtraction connection). In our examination we have fundamentally focusssed on the identification of shadow from the facilitating so as to move article through a video observation test multi-wavelet choice and client configurable difference parameters. In our test client can pick the diverse wavelets and change parameters. Edge model based super determination technique is utilized to improve results. Additionally the impact of advanced watermarking is concentrated on for the super-determined VOP(Video articles planes). Various experiments have been proposed and figured out a best system for video reconnaissance application. Our proposed super determination (SR) system gives preferred results over bilinear and bi-cubic routines

Activity profiling for minimally invasive surgery

Imperial Users onl

Carried baggage detection and recognition in video surveillance with foreground segmentation

Security cameras installed in public spaces or in private organizations continuously record video data with the aim of detecting and preventing crime. For that reason, video content analysis applications, either for real time (i.e. analytic) or post-event (i.e. forensic) analysis, have gained high interest in recent years. In this thesis, the primary focus is on two key aspects of video analysis, reliable moving object segmentation and carried object detection & identification. A novel moving object segmentation scheme by background subtraction is presented in this thesis. The scheme relies on background modelling which is based on multi-directional gradient and phase congruency. As a post processing step, the detected foreground contours are refined by classifying the edge segments as either belonging to the foreground or background. Further contour completion technique by anisotropic diffusion is first introduced in this area. The proposed method targets cast shadow removal, gradual illumination change invariance, and closed contour extraction. A state of the art carried object detection method is employed as a benchmark algorithm. This method includes silhouette analysis by comparing human temporal templates with unencumbered human models. The implementation aspects of the algorithm are improved by automatically estimating the viewing direction of the pedestrian and are extended by a carried luggage identification module. As the temporal template is a frequency template and the information that it provides is not sufficient, a colour temporal template is introduced. The standard steps followed by the state of the art algorithm are approached from a different extended (by colour information) perspective, resulting in more accurate carried object segmentation. The experiments conducted in this research show that the proposed closed foreground segmentation technique attains all the aforementioned goals. The incremental improvements applied to the state of the art carried object detection algorithm revealed the full potential of the scheme. The experiments demonstrate the ability of the proposed carried object detection algorithm to supersede the state of the art method

A Comprehensive Review of Vehicle Detection Techniques Under Varying Moving Cast Shadow Conditions Using Computer Vision and Deep Learning

Design of a vision-based traffic analytic system for urban traffic video scenes has a great potential in context of Intelligent Transportation System (ITS). It offers useful traffic-related insights at much lower costs compared to their conventional sensor based counterparts. However, it remains a challenging problem till today due to the complexity factors such as camera hardware constraints, camera movement, object occlusion, object speed, object resolution, traffic flow density, and lighting conditions etc. ITS has many applications including and not just limited to queue estimation, speed detection and different anomalies detection etc. All of these applications are primarily dependent on sensing vehicle presence to form some basis for analysis. Moving cast shadows of vehicles is one of the major problems that affects the vehicle detection as it can cause detection and tracking inaccuracies. Therefore, it is exceedingly important to distinguish dynamic objects from their moving cast shadows for accurate vehicle detection and recognition. This paper provides an in-depth comparative analysis of different traffic paradigm-focused conventional and state-of-the-art shadow detection and removal algorithms. Till date, there has been only one survey which highlights the shadow removal methodologies particularly for traffic paradigm. In this paper, a total of 70 research papers containing results of urban traffic scenes have been shortlisted from the last three decades to give a comprehensive overview of the work done in this area. The study reveals that the preferable way to make a comparative evaluation is to use the existing Highway I, II, and III datasets which are frequently used for qualitative or quantitative analysis of shadow detection or removal algorithms. Furthermore, the paper not only provides cues to solve moving cast shadow problems, but also suggests that even after the advent of Convolutional Neural Networks (CNN)-based vehicle detection methods, the problems caused by moving cast shadows persists. Therefore, this paper proposes a hybrid approach which uses a combination of conventional and state-of-the-art techniques as a pre-processing step for shadow detection and removal before using CNN for vehicles detection. The results indicate a significant improvement in vehicle detection accuracies after using the proposed approach

Building Development Monitoring in Multitemporal Remotely Sensed Image Pairs with Stochastic Birth-Death Dynamics

International audienceIn this paper we introduce a new probabilistic method which integrates building extraction with change detection in remotely sensed image pairs. A global optimization process attempts to find the optimal configuration of buildings, considering the observed data, prior knowledge, and interactions between the neighboring building parts. We present methodological contributions in three key issues: (1) We implement a novel object-change modeling approach based on Multitemporal Marked Point Processes, which simultaneously exploits low level change information between the time layers and object level building description to recognize and separate changed and unaltered buildings. (2) To answering the challenges of data heterogeneity in aerial and satellite image repositories, we construct a flexible hierarchical framework which can create various building appearance models from different elementary feature based modules. (3) To simultaneously ensure the convergence, optimality and computation complexity constraints raised by the increased data quantity, we adopt the quick Multiple Birth and Death optimization technique for change detection purposes, and propose a novel non-uniform stochastic object birth process, which generates relevant objects with higher probability based on low-level image features

Deep learning for land cover and land use classification

Recent advances in sensor technologies have witnessed a vast amount of very fine spatial resolution (VFSR) remotely sensed imagery being collected on a daily basis. These VFSR images present fine spatial details that are spectrally and spatially complicated, thus posing huge challenges in automatic land cover (LC) and land use (LU) classification. Deep learning reignited the pursuit of artificial intelligence towards a general purpose machine to be able to perform any human-related tasks in an automated fashion. This is largely driven by the wave of excitement in deep machine learning to model the high-level abstractions through hierarchical feature representations without human-designed features or rules, which demonstrates great potential in identifying and characterising LC and LU patterns from VFSR imagery. In this thesis, a set of novel deep learning methods are developed for LC and LU image classification based on the deep convolutional neural networks (CNN) as an example. Several difficulties, however, are encountered when trying to apply the standard pixel-wise CNN for LC and LU classification using VFSR images, including geometric distortions, boundary uncertainties and huge computational redundancy. These technical challenges for LC classification were solved either using rule-based decision fusion or through uncertainty modelling using rough set theory. For land use, an object-based CNN method was proposed, in which each segmented object (a group of homogeneous pixels) was sampled and predicted by CNN with both within-object and between-object information. LU was, thus, classified with high accuracy and efficiency. Both LC and LU formulate a hierarchical ontology at the same geographical space, and such representations are modelled by their joint distribution, in which LC and LU are classified simultaneously through iteration. These developed deep learning techniques achieved by far the highest classification accuracy for both LC and LU, up to around 90% accuracy, about 5% higher than the existing deep learning methods, and 10% greater than traditional pixel-based and object-based approaches. This research made a significant contribution in LC and LU classification through deep learning based innovations, and has great potential utility in a wide range of geospatial applications

Shadow removal utilizing multiplicative fusion of texture and colour features for surveillance image

Automated surveillance systems often identify shadows as parts of a moving object which jeopardized subsequent image processing tasks such as object identification and tracking. In this thesis, an improved shadow elimination method for an indoor surveillance system is presented. This developed method is a fusion of several image processing methods. Firstly, the image is segmented using the Statistical Region Merging algorithm to obtain the segmented potential shadow regions. Next, multiple shadow identification features which include Normalized Cross-Correlation, Local Color Constancy and Hue-Saturation-Value shadow cues are applied on the images to generate feature maps. These feature maps are used for identifying and removing cast shadows according to the segmented regions. The video dataset used is the Autonomous Agents for On-Scene Networked Incident Management which covers both indoor and outdoor video scenes. The benchmarking result indicates that the developed method is on-par with several normally used shadow detection methods. The developed method yields a mean score of 85.17% for the video sequence in which the strongest shadow is present and a mean score of 89.93% for the video having the most complex textured background. This research contributes to the development and improvement of a functioning shadow eliminator method that is able to cope with image noise and various illumination changes

Change detection in combination with spatial models and its effectiveness on underwater scenarios

This thesis proposes a novel change detection approach for underwater scenarios and combines it with different especially developed spatial models, this allows accurate and spatially coherent detection of any moving objects with a static camera in arbitrary environments. To deal with the special problems of underwater imaging pre-segmentations based on the optical flow and other special adaptions were added to the change detection algorithm so that it can better handle typical underwater scenarios like a scene crowded by a whole fish swarm

An Embedded Marked Point Process Framework for Three-Level Object Population Analysis

In this paper we introduce a probabilistic approach for extracting complex hierarchical object structures from digital images used by various vision applications. The proposed framework extends conventional Marked Point Process (MPP) models by (i) admitting object-subobject ensembles in parent-child relationships and (ii) allowing corresponding objects to form coherent object groups, by a Bayesian segmentation of the population. Different from earlier, highly domain specific attempts on MPP generalization, the proposed model is defined at an abstract level, providing clear interfaces for applications in various domains. We also introduce a global optimization process for the multi-layer framework for finding optimal entity configurations, considering the observed data, prior knowledge, and interactions between the neighboring and the hierarchically related objects. The proposed method is demonstrated in three different application areas: built in area analysis in remotely sensed images, traffic monitoring on airborne and mobile laser scanning (Lidar) data and optical circuit inspection. A new benchmark database is published for the three test cases, and the model's performance is quantitatively evaluated