From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Nowadays, mobile devices are an important part of our everyday lives since they enable us to access a large variety of ubiquitous services. In recent years, the availability of these ubiquitous and mobile services has significantly increased due to the different form of connectivity provided by mobile devices, such as GSM, GPRS, Bluetooth and Wi-Fi. In the same trend, the number and typologies of vulnerabilities exploiting these services and communication channels have increased as well. Therefore, smartphones may now represent an ideal target for malware writers. As the number of vulnerabilities and, hence, of attacks increase, there has been a corresponding rise of security solutions proposed by researchers. Due to the fact that this research field is immature and still unexplored in depth, with this paper we aim to provide a structured and comprehensive overview of the research on security solutions for mobile devices. This paper surveys the state of the art on threats, vulnerabilities and security solutions over the period 2004-2011, by focusing on high-level attacks, such those to user applications. We group existing approaches aimed at protecting mobile devices against these classes of attacks into different categories, based upon the detection principles, architectures, collected data and operating systems, especially focusing on IDS-based models and tools. With this categorization we aim to provide an easy and concise view of the underlying model adopted by each approach.

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A Survey on Security for Mobile Devices

How deep learning extracts and learns leaf features for plant classification

Species knowledge is essential for protecting biodiversity. The identification of plants by conventional keys is complex, time consuming, and due to the use of specific botanical terms frustrating for non-experts. This creates a hard to overcome hurdle for novices interested in acquiring species knowledge. Today, there is an increasing interest in automating the process of species identification.
 The availability and ubiquity of relevant technologies, such as, digital cameras and mobile devices, the remote access to databases, new techniques in image processing and pattern recognition let the idea of automated species identification become reality. This paper is the first systematic literature review with the aim of a thorough analysis and comparison of primary studies on computer vision approaches for plant species identification. We identified 120 peer-reviewed studies, selected through a multi-stage process, published in the last 10 years (2005–2015). After a careful analysis of these studies, we describe the applied methods categorized according to the studied plant organ, and the studied features, i.e., shape, texture, color, margin, and vein structure. Furthermore, we compare methods based on classification accuracy achieved on publicly available datasets. Our results are relevant to researches in ecology as well as computer vision for their ongoing research. The systematic and concise overview will also be helpful for beginners in those research fields, as they can use the comparable analyses of applied methods as a guide in this complex activity.

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Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review

High resolution and high throughput genotype to phenotype studies in plants are underway to accelerate breeding of climate ready crops. In the recent years, deep learning techniques and in particular Convolutional Neural Networks (CNNs), Recurrent Neural Networks and Long-Short Term Memories (LSTMs), have shown great success in visual data recognition, classification, and sequence learning tasks. More recently, CNNs have been used for plant classification and phenotyping, using individual static images of the plants. On the other hand, dynamic behavior of the plants as well as their growth has been an important phenotype for plant biologists, and this motivated us to study the potential of LSTMs in encoding these temporal information for the accession classification task, which is useful in automation of plant production and care. In this paper, we propose a CNN-LSTM framework for plant classification of various genotypes. Here, we exploit the power of deep CNNs for automatic joint feature and classifier learning, compared to using hand-crafted features. In addition, we leverage the potential of LSTMs to study the growth of the plants and their dynamic behaviors as important discriminative phenotypes for accession classification. Moreover, we collected a dataset of time-series image sequences of four accessions of Arabidopsis, captured in similar imaging conditions, which could be used as a standard benchmark by researchers in the field. We made this dataset publicly available. The results provide evidence of the benefits of our accession classification approach over using traditional hand-crafted image analysis features and other accession classification frameworks. We also demonstrate that utilizing temporal information using LSTMs can further improve the performance of the system. The proposed framework can be used in other applications such as in plant classification given the environment conditions or in distinguishing diseased plants from healthy ones.

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Deep phenotyping: deep learning for temporal phenotype/genotype classification.

Automatic classification of plants based on their leaves

This paper presents vulnerabilities of UMTS access domain security architecture. The security architecture of UMTS offers some protection against known threats including false base station attacks, man-in-the-middle attacks and replay attacks. The system also successfully ensures user data confidentiality and signaling data integrity. However, a few novel vulnerabilities have been identified in this paper. It has been shown that modification of unprotected initial messages prior to the security mode command may result in DoS and man-in-the-middle attacks. Non-integrity protection of rrcConnectionReject message can also be exploited to launch DoS attack. Clear transmission of IMSI on some occasions is a violation of user identity/location confidentiality and user traceability. This exposed IMSI can be exploited for new attacks.

Vulnerabilities of UMTS Access Domain Security Architecture

This paper presents techniques for segmentation and change classification using logistic regression. The research was conducted on SPOT 5 multispectral multitemporal images covering the 2010 floods in Pakistan. Segmentation was performed to extract the built up area (BUA) from the satellite images and change detection was performed to find the damaged BUA. The damaged area was classified into three categories based on the extent of damage. The segmentation results were validated using statistical measures like precision, recall, and dice coefficient on available ground truth. The results of change classification were compared and found consistent with the manual assessment report produced by UNO experts using Worldview 1 satellite imagery with submeter resolution. The proposed scheme and results give an indication that SPOT 5 imagery can be used for fast automatic damage assessment and classification immediately after a natural calamity. The proposed change detection technique was also applied on Unites States Geographical Survey dataset. We compared our change detection results with established methods like change vector analysis, Principal component analysis using K-means and commercially available software Erdas Imagine on both the above-mentioned datasets. The comparison results suggest that our proposed algorithm performs better than the other methods.

Segmentation and Classification Using Logistic Regression in Remote Sensing Imagery

The automatic extraction of contour lines and generation of digital elevation models (DEMs) from topographic maps is one of the challenging subjects mostly because of aliasing, false colors, closely spaced lines and other features causing intersection or overlapping. In this paper we present an algorithm to extract contour lines from colored images of scanned topographic maps. In our approach, we first segment the color image using adaptive thresholding to extract basic contour structure. Noise in the images is removed utilizing morphological operation. Next, the contour lines are reduced up to unitary thickness using Zhang’s thinning algorithm. The bifurcation and holes that result after thinning are removed using different masks. After thinning, end points of the broken contours are identified and best candidate for connection is determined, this is performed by analyzing the Euclidean distance and direction of end points near the gap. Then broken contour lines are joined employing curve fitting technique. The performance of the algorithm is tested on several samples of topographic maps and results show good segmentation of the contour lines. This automatic extraction algorithm for contour lines from topographic maps can save significant amount of time and labor as well as improving the accuracy of the contour line extraction.

Automatic Extraction of Contour Lines from Topographic Maps

In this paper, we present a new multimodal image registration technique established on elastodynamics notion. The main idea behind this concept is the progression of waves on an elastic body as soon as it is disturbed from its initial rest state. We propose to solve the multimodal registration problem by modeling the non-linear deformations as elastic waves and iteratively solving the elastodynamics wave equation to estimate the transformation. The inertial force in elastodynamics model is computed as the gradient of mutual information which considers the statistical relationship between the intensities of the images acquired using different imaging modalities. We tested our method on T1–T2 weighted MR brain image pairs and MR-CT brain image pairs. The proposed registration technique was compared against a variant of demons method proposed for multimodal images. The registration results were analyzed by examining the overlay images and by computing the normalized mutual information. The qualitative and quantitative analysis proved that our proposed method registers the images better than the compared method.

Muhammad Faisal Khan

Papers

Automatic classification of plants based on their leaves

Vulnerabilities of UMTS Access Domain Security Architecture

Segmentation and Classification Using Logistic Regression in Remote Sensing Imagery

Automatic Extraction of Contour Lines from Topographic Maps

Multimodal non-rigid image registration based on elastodynamics