Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

The Theory of Island Biogeography

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Biomechanics And Motor Control Of Human Movement

Low-level saliency cues or priors do not produce good enough saliency detection results especially when the salient object presents in a low-contrast background with confusing visual appearance. This issue raises a serious problem for conventional approaches. In this paper, we tackle this problem by proposing a multi-context deep learning framework for salient object detection. We employ deep Convolutional Neural Networks to model saliency of objects in images. Global context and local context are both taken into account, and are jointly modeled in a unified multi-context deep learning framework. To provide a better initialization for training the deep neural networks, we investigate different pre-training strategies, and a task-specific pre-training scheme is designed to make the multi-context modeling suited for saliency detection. Furthermore, recently proposed contemporary deep models in the ImageNet Image Classification Challenge are tested, and their effectiveness in saliency detection are investigated. Our approach is extensively evaluated on five public datasets, and experimental results show significant and consistent improvements over the state-of-the-art methods.

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Saliency detection by multi-context deep learning

One of the principal problems in image forensics is determining if a particular image is authentic or not. This can be a crucial task when images are used as basic evidence to influence judgment like, for example, in a court of law. To carry out such forensic analysis, various technological instruments have been developed in the literature. In this paper, the problem of detecting if an image has been forged is investigated; in particular, attention has been paid to the case in which an area of an image is copied and then pasted onto another zone to create a duplication or to cancel something that was awkward. Generally, to adapt the image patch to the new context a geometric transformation is needed. To detect such modifications, a novel methodology based on scale invariant features transform (SIFT) is proposed. Such a method allows us to both understand if a copy-move attack has occurred and, furthermore, to recover the geometric transformation used to perform cloning. Extensive experimental results are presented to confirm that the technique is able to precisely individuate the altered area and, in addition, to estimate the geometric transformation parameters with high reliability. The method also deals with multiple cloning.

A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery

From the Publisher:
Handbook of Computer Vision Algorithms in Image Algebra provides engineers, scientists, and students with an introduction to image algebra and presents detailed descriptions of over 80 fundamental computer vision techniques. The book also introduces the portable iac++ library, which supports image algebra programming in the C++ language.

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Handbook of Computer Vision Algorithms in Image Algebra

Reconfigurable hardware in the form of field programmable gate arrays (FPGAs) has been proposed as a way of obtaining high performance for computationally intensive DSP applications such as image processing (IP), even under real time requirements. The inherent reprogrammability of FPGAs gives them some of the flexibility of software while keeping the performance advantages of an application specific solution. However, a major disadvantage of FPGAs is their low level programming model. To bridge the gap between these two levels, the authors present a high level software environment for FPGA-based image processing, which aims to hide hardware details as much as possible from the user. Their approach is to provide a very high level image processing coprocessor (IPC) with a core instruction set based on the operations of image algebra. The environment includes a generator which generates optimised architectures for specific user-defined operations.

Design and implementation of a high level programming environment for FPGA-based image processing

Image processing is often considered a good candidate for the application of parallel processing because of the large volumes of data and the complex algorithms commonly encountered. This paper presents a tutorial introduction to the field of parallel image processing. After introducing the classes of parallel processing a brief review of architectures for parallel image processing is presented. Software design for low-level image processing and parallelism in high-level image processing are discussed and an application of parallel processing to handwritten postcode recognition is described. The paper concludes with a look at future technology and market trends.

Parallel architectures for image processing

This paper introduces an automated computer- assisted system for the diagnosis of cervical intraepithelial neoplasia (CIN) using ultra-large cervical histological digital slides. The system contains two parts: the segmentation of squamous epithelium and the diagnosis of CIN. For the segmentation, to reduce processing time, a multiresolution method is developed. The squamous epithelium layer is first segmented at a low (2X) resolution. The boundaries are further fine tuned at a higher (20X) resolution. The block-based segmentation method uses robust texture feature vectors in combination with support vector machines (SVMs) to perform classification. Medical rules are finally applied. In testing, segmentation using 31 digital slides achieves 94.25% accuracy. For the diagnosis of CIN, changes in nuclei structure and morphology along lines perpendicular to the main axis of the squamous epithelium are quantified and classified. Using multi-category SVM, perpendicular lines are classified into Normal, CIN I, CIN II, and CIN III. The robustness of the system in term of regional diagnosis is measured against pathologists' diagnoses and inter-observer variability between two pathologists is considered. Initial results suggest that the system has potential as a tool both to assist in pathologists' diagnoses, and in training.

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Assisted Diagnosis of Cervical Intraepithelial Neoplasia (CIN)

Temporal dynamics and speaker characteristics are two important features of speech that distinguish speech from noise In this paper, we propose a method to maximally extract these two features of speech for speech enhancement We demonstrate that this can reduce the requirement for prior information about the noise, which can be difficult to estimate for fast-varying noise Given noisy speech, the new approach estimates clean speech by recognizing long segments of the clean speech as whole units In the recognition, clean speech sentences, taken from a speech corpus, are used as examples Matching segments are identified between the noisy sentence and the corpus sentences The estimate is formed by using the longest matching segments found in the corpus sentences Longer speech segments as whole units contain more distinct dynamics and richer speaker characteristics, and can be identified more accurately from noise than shorter speech segments Therefore, estimation based on the longest recognized segments increases the noise immunity and hence the estimation accuracy The new approach consists of a statistical model to represent up to sentence-long temporal dynamics in the corpus speech, and an algorithm to identify the longest matching segments between the noisy sentence and the corpus sentences The algorithm is made more robust to noise uncertainty by introducing missing-feature based noise compensation into the corpus sentences Experiments have been conducted on the TIMIT database for speech enhancement from various types of nonstationary noise including song, music, and crosstalk speech The new approach has shown improved performance over conventional enhancement algorithms in both objective and subjective evaluations

A corpus-based approach to speech enhancement from nonstationary noise.

In this paper, we introduce a novel approach to face recognition which simultaneously tackles three combined challenges: 1) uneven illumination; 2) partial occlusion; and 3) limited training data. The new approach performs lighting normalization, occlusion de-emphasis and finally face recognition, based on finding the largest matching area (LMA) at each point on the face, as opposed to traditional fixed-size local area-based approaches. Robustness is achieved with novel approaches for feature extraction, LMA-based face image comparison and unseen data modeling. On the extended YaleB and AR face databases for face identification, our method using only a single training image per person, outperforms other methods using a single training image, and matches or exceeds methods which require multiple training images. On the labeled faces in the wild face verification database, our method outperforms comparable unsupervised methods. We also show that the new method performs competitively even when the training images are corrupted.

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Danny Crookes

Papers

Design and implementation of a high level programming environment for FPGA-based image processing

Parallel architectures for image processing

Assisted Diagnosis of Cervical Intraepithelial Neoplasia (CIN)

A corpus-based approach to speech enhancement from nonstationary noise.

Largest Matching Areas for Illumination and Occlusion Robust Face Recognition