Data Mining Practical Machine Learning Tools and Techniques

C4.5: Programs for Machine Learning (書評)

Several metrics have been proposed in literature to assess the perceptual quality of two-dimensional images. However, no similar effort has been devoted to quality assessment of stereoscopic images. Therefore, in this paper, we review the different issues related to 3D visualization, and we propose a quality metric for the assessment of stereopairs using the fusion of 2D quality metrics and of the depth information. The proposed metric is evaluated using the SAMVIQ methodology for subjective assessment. Specifically, distortions deriving from coding are taken into account and the quality degradation of the stereopair is estimated by means of subjective tests.

https://hal.archives-ouvertes.fr/hal-00362891/document

Quality Assessment of Stereoscopic Images

Research efforts on 3DTV technology have been strengthened worldwide recently, covering the whole media processing chain from capture to display. Different 3DTV systems rely on different 3D scene representations that integrate various types of data. Efficient coding of these data is crucial for the success of 3DTV. Compression of pixel-type data including stereo video, multiview video, and associated depth or disparity maps extends available principles of classical video coding. Powerful algorithms and open international standards for multiview video coding and coding of video plus depth data are available and under development, which will provide the basis for introduction of various 3DTV systems and services in the near future. Compression of 3D mesh models has also reached a high level of maturity. For static geometry, a variety of powerful algorithms are available to efficiently compress vertices and connectivity. Compression of dynamic 3D geometry is currently a more active field of research. Temporal prediction is an important mechanism to remove redundancy from animated 3D mesh sequences. Error resilience is important for transmission of data over error prone channels, and multiple description coding (MDC) is a suitable way to protect data. MDC of still images and 2D video has already been widely studied, whereas multiview video and 3D meshes have been addressed only recently. Intellectual property protection of 3D data by watermarking is a pioneering research area as well. The 3D watermarking methods in the literature are classified into three groups, considering the dimensions of the main components of scene representations and the resulting components after applying the algorithm. In general, 3DTV coding technology is maturating. Systems and services may enter the market in the near future. However, the research area is relatively young compared to coding of other types of media. Therefore, there is still a lot of room for improvement and new development of algorithms.

Coding Algorithms for 3DTV—A Survey

We present a survey on maritime object detection and tracking approaches, which are essential for the development of a navigational system for autonomous ships. The electro-optical (EO) sensor considered here is a video camera that operates in the visible or the infrared spectra, which conventionally complements radar and sonar for situational awareness at sea and has demonstrated its effectiveness over the last few years. This paper provides a comprehensive overview of various approaches of video processing for object detection and tracking in the maritime environment. We follow an approach-based taxonomy wherein the advantages and limitations of each approach are compared. The object detection system consists of the following modules: horizon detection, static background subtraction, and foreground segmentation. Each of these has been studied extensively in maritime situations and has been shown to be challenging due to the presence of background motion especially due to waves and wakes. The key processes involved in object tracking include video frame registration, dynamic background subtraction, and the object tracking algorithm itself. The challenges for robust tracking arise due to camera motion, dynamic background, and low contrast of tracked object, possibly due to environmental degradation. The survey also discusses multisensor approaches and commercial maritime systems that use EO sensors. The survey also highlights methods from computer vision research, which hold promise to perform well in maritime EO data processing. Performance of several maritime and computer vision techniques is evaluated on Singapore Maritime Dataset.

Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey

Efficient storing and retrieval of medical images has direct impact on reducing costs and improving access in cloud-based health care services. JPEG 2000 is currently the commonly used compression format for medical images shared using the DICOM standard. However, new formats such as high efficiency video coding (HEVC) can provide better compression efficiency compared to JPEG 2000. Furthermore, JPEG 2000 is not suitable for efficiently storing image series and 3-D imagery. Using HEVC, a single format can support all forms of medical images. This paper presents the use of HEVC for diagnostically acceptable medical image compression, focusing on compression efficiency compared to JPEG 2000. Diagnostically acceptable lossy compression and complexity of high bit-depth medical image compression are studied. Based on an established medically acceptable compression range for JPEG 2000, this paper establishes acceptable HEVC compression range for medical imaging applications. Experimental results show that using HEVC can increase the compression performance, compared to JPEG 2000, by over 54%. Along with this, a new method for reducing computational complexity of HEVC encoding for medical images is proposed. Results show that HEVC intra encoding complexity can be reduced by over 55% with negligible increase in file size.

High Bit-Depth Medical Image Compression With HEVC

This paper proposes a hybrid foreground object detection method suitable for the marine surveillance applications. Our approach combines an existing foreground object detection method with an image color segmentation technique to improve accuracy. The foreground segmentation method employs a Bayesian decision framework, while the color segmentation part is graph-based and relies on the local variation of edges. We also establish the set of requirements any practical marine surveillance algorithm should fulfill, and show that our method conforms to these requirements. Experiments show good results in the domain of marine surveillance sequences.

/pdf/a-hybrid-color-based-foreground-object-detection-method-for-33j76bizob.pdf

A hybrid color-based foreground object detection method for automated marine surveillance

High Efficiency Video Coding (HEVC) is the new video coding standard recently approved by ISO and ITU. HEVC allows a bit rate reduction greater than a 50% with respect to its predecessor, the H.264/AVC, offering the same perceptual quality, by means of a set of new tools that have been introduced. Compared with the current state of the art in image coding, such as JPEG, JPEG2000 or the new JPEG XR, the new Intra Frame coding performs a high compression process in the "All-Intra" mode. All these improvements are at expense of a high computational cost, making it considerably difficult to implement in real time. Hence, this paper presents a mechanism that can be used by the RDO algorithm to select the optimal coding block size for Intra-Prediction, by using a data mining classifier, based on a previous training. Experimental results show that the proposed algorithm can achieve a 30% of Time Savings over a wide range of high resolution sequences (Class A, B and F), with a negligible loss of coding efficiency.

Fast partitioning algorithm for HEVC Intra frame coding using machine learning

Digital video is being used widely in a variety of applications such as entertainment, surveillance and security. Large amount of video in surveillance and security requires systems capable of processing video to automatically detect and recognize events to alleviate the load on humans and enable preventive actions when events are detected. The main objective of this work is the analysis of computer vision techniques and algorithms to perform automatic detection of specific events in video sequences. This paper presents a surveillance system based on motion analysis and introduces the idea of event probability zones. Advantages, limitations, capabilities and possible solution alternatives are also discussed. The result is a system capable of detecting events of objects moving in opposing direction in a predefined context or running in the scene; the results showed precision greater than 50% and recall greater than 80%.

/pdf/event-detection-in-video-using-motion-analysis-2jagbtga98.pdf

Event detection in video using motion analysis

A novel encryption model for digital videos is presented. The model relies on the encryption-compression duality of certain types of permutations acting on video frames. In essence, the proposed encryption process preserves the spatial correlation and, as such, can be applied prior to the compression stage of a spatial-only video encoder. Several algorithmic modes of the proposed model targeted for different application requirements are presented and analyzed in terms of security and performance. Experimental results are generated for a number of standard benchmark sequences showing that the proposed method, in addition to providing confidentiality, preserves or improves the compression ratio.

/pdf/digital-video-encryption-algorithms-based-on-correlation-53rxa0gwpa.pdf

Hari Kalva

Papers

High Bit-Depth Medical Image Compression With HEVC

A hybrid color-based foreground object detection method for automated marine surveillance

Fast partitioning algorithm for HEVC Intra frame coding using machine learning

Event detection in video using motion analysis

Digital Video Encryption Algorithms Based on Correlation-Preserving Permutations