Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

We conduct an exhaustive survey of image thresholding methods, categorize them, express their formulas under a uniform notation, and finally carry their performance comparison. The thresholding methods are categorized according to the information they are exploiting, such as histogram shape, measurement space clustering, entropy, object attributes, spatial correlation, and local gray-level surface. 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images. The comparison is based on the combined performance measures. We identify the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications. © 2004 SPIE and IS&T. (DOI: 10.1117/1.1631316)

/pdf/survey-over-image-thresholding-techniques-and-quantitative-2ekadyqjhj.pdf

Survey over image thresholding techniques and quantitative performance evaluation

This paper describes a new approach to low level image processing; in particular, edge and corner detection and structure preserving noise reduction.

Non-linear filtering is used to define which parts of the image are closely related to each individual pixel; each pixel has associated with it a local image region which is of similar brightness to that pixel. The new feature detectors are based on the minimization of this local image region, and the noise reduction method uses this region as the smoothing neighbourhood. The resulting methods are accurate, noise resistant and fast.

Details of the new feature detectors and of the new noise reduction method are described, along with test results.

SUSAN—A New Approach to Low Level Image Processing

Digital watermarking is a key ingredient to copyright protection. It provides a solution to illegal copying of digital material and has many other useful applications such as broadcast monitoring and the recording of electronic transactions. Now, for the first time, there is a book that focuses exclusively on this exciting technology. Digital Watermarking covers the crucial research findings in the field: it explains the principles underlying digital watermarking technologies, describes the requirements that have given rise to them, and discusses the diverse ends to which these technologies are being applied. As a result, additional groundwork is laid for future developments in this field, helping the reader understand and anticipate new approaches and applications.

Digital Watermarking

In digital image processing, thresholding is a well-known technique for image segmentation. Because of its wide applicability to other areas of the digital image processing, quite a number of thresholding methods have been proposed over the years. In this paper, we present a survey of thresholding techniques and update the earlier survey work by Weszka (Comput. Vision Graphics & Image Process 7, 1978 , 259–265) and Fu and Mu (Pattern Recognit. 13, 1981 , 3–16). We attempt to evaluate the performance of some automatic global thresholding methods using the criterion functions such as uniformity and shape measures. The evaluation is based on some real world images.

http://pequan.lip6.fr/~bereziat/pima/2012/seuillage/sahoo88.pdf

A survey of thresholding techniques

A new method for data hiding in binary images based on block pattern coding and dynamic programming with distortion-minimizing capabilities is proposed. Up to three message data bits can be embedded into each 2 × 2 block in an input image by changing the block's pixel pattern into another, which represents the value of the message data bits as a code according to a block pattern encoding table. And extraction of hidden message data is accomplished by block pattern decoding. To minimize the resulting image distortion, two optimization techniques are proposed. The first is to use multiple block pattern encoding tables, from which an optimal one is selected specifically for each input image, and the second is to use a dynamic programming algorithm to divide the message data into bit segments for optimal embedding in a sense of minimizing the number of binary bit flippings. Accordingly, not only more data bits can be embedded in an image block on the average, but the resulting image distortion is also reduced in an optimal way. Experimental results are also included to show the effectiveness of the proposed approach.

Data Hiding in Binary Images with Distortion-Minimizing Capabilities by Optimal Block Pattern Coding and Dynamic Programming Techniques

An omni-vision-based localization method for automatic helicopter landing assistance on a helipad with a circled H-shape is proposed. The landing process includes two stages: approaching and alignment. Three types of image features, circle, line, and point, are used to derive skillfully analytic equations for computing the helicopter height, distance, and orientation with respect to the landing site. Experimental results with good location estimation accuracy are also shown.†

An omni-vision based localization method for automatic helicopter landing assistance on standard helipads

A vision-based approach to unsupervised learning of the indoor environment for autonomous land vehicle (ALV) navigation is proposed. The ALV may, without human's involvement, self-navigate systematically in an unexplored closed environment, collect the information of the environment features, and then build a top-view map of the environment for later planned navigation or other applications. The learning system consists of three subsystems: a feature location subsystem, a model management subsystem, and an environment exploration subsystem. The feature location subsystem processes input images, and calculates the locations of the local features and the ALV by model matching techniques. To facilitate feature collection, two laser markers are mounted on the vehicle which project laser light on the corridor walls to form easily detectable line and corner features. The model management subsystem attaches the local model into a global one by merging matched corner pairs as well as line segment pairs. The environment exploration subsystem guides the ALV to explore the entire navigation environment by using the information of the learned model and the current ALV location. The guidance scheme is based on the use of a pushdown transducer derived from automata theory. A prototype learning system was implemented on a real vehicle, and simulations and experimental results in real environments show the feasibility of the proposed approach.

http://www.asia.edu.tw/Main_pages/about/Former_president_TsaiWH_publications/Journal%20Paper%20PDFs/Chen_&_Tsai_Robotics_&_CIM_1999.pdf

A new approach to vision-based unsupervised learning of unexplored indoor environment for autonomous land vehicle navigation ☆

A new authentication method utilizing the secret sharing technique to resist attacks with a data recovery capability for binary document images in PNG format is proposed. An authentication signal is generated for each block of a cover image, which together with the block content is transformed into several shares using the Shamir secret sharing scheme. The shares then are embedded into the alpha channel plane of the input PNG image in a carefully designed manner. In the process of image authentication, an image block is marked as tampered if the authentication signal computed from the current block content does not match that extracted from the shares embedded in the alpha channel plane. Data recovery is conducted for each tampered block by a reverse Shamir scheme after collecting enough shares from unmarked blocks. Experimental results prove the effectiveness of the proposed method for real applications.

http://mis2.nkmu.edu.tw/mis/papers/會議(6)%20台北國際會議_Authentication%20of%20Binary%20Document%20Images_ICSSE.pdf

Authentication of binary document images in PNG format based on a secret sharing technique

Moire patterns are distortions on the results of scanning printed documents. However, the patterns can be utilized in document image segmentation and quality improvement. The moire phenomenon comes from sampling periodical structures in images, such as halftone screens, color components, and text galleys which often appear in printed magazines and newspapers. The generated moire patterns appear in the scanning result in the form of obvious periodical patterns, color skew, and color noise on the edges of artworks. The moire pattern degrades the scanning result and makes document analysis more difficult. A new approach to document image segmentation and quality improvement by moire pattern analysis is proposed. A scanning resolution, called the conductor of screen sharing, is proposed to control the moire pattern. With the resolution, moire patterns are generated and enhanced in certain designed areas in the frequency domain. Then, a logical filter, called the comb filter, is proposed to detect the moire pattern. The new method, which is based on the sampling theory and moire analysis in the frequency domain, is actually performed in the spatial domain by re-sampling and logical filtering. The proposed method can efficiently extract gray or color pictures, artworks, and text paragraphs in printed documents. Moreover, the moire patterns on the segmented document components can be easily suppressed. The suppression yields better image quality for further analysis and image compression. Experimental results are shown to demonstrate the feasibility of the proposed approach.

https://www.asia.edu.tw/Main_pages/about/Former_president_TsaiWH_publications/Journal%20Paper%20PDFs/Yang_&_Tsai_Sig_Proc_Img_Comm_2000.pdf

Wen-Hsiang Tsai

Papers

Data Hiding in Binary Images with Distortion-Minimizing Capabilities by Optimal Block Pattern Coding and Dynamic Programming Techniques

An omni-vision based localization method for automatic helicopter landing assistance on standard helipads

A new approach to vision-based unsupervised learning of unexplored indoor environment for autonomous land vehicle navigation ☆

Authentication of binary document images in PNG format based on a secret sharing technique

Document image segmentation and quality improvement by moiré pattern analysis