Estimating hidden message length in binary image embedded by using boundary pixels steganography

In this paper, we propose a new steganalytic method to detect the message hidden in a black and white image using the steganographic technique developed by Liang, Wang and Zhang. Our detection method estimates the length of hidden message embedded in a binary image. Although the hidden message embedded is visually imperceptible, it changes some image statistic (such as inter-pixels correlation). Based on this observation, we first derive the 512 patterns histogram from the boundary pixels as the distinguishing statistic, then we compute the histogram difference to determine the changes of the 512 patterns histogram induced by the embedding operation. Finally we propose histogram quotient to estimate the length of the embedded message. Experimental results confirm that the proposed method can effectively and reliably detect the length of the embedded message.

Appetite sensations and satiety quotient : predictors of energy intake and weight loss

PURPOSE: The aim of this study was to further evaluate the validity and clinical meaningfulness of appetite sensations to predict overall energy intake as well as body weight loss. METHODS: Men (n=176) and women (n=139) involved in six weight loss studies were selected to participate in this study. Visual analogue scales were used to measure appetite sensations before and after a fixed test meal. Fasting appetite sensations, 1 h post-prandial area under the curve (AUC) and the satiety quotient (SQ) were used as predictors of energy intake and body weight loss. Two separate measures of energy intake were used: a buffet style ad libitum test lunch and a three-day self-report dietary record. RESULTS: One-hour post-prandial AUC for all appetite sensations represented the strongest predictors of ad libitum test lunch energy intake (p0.001). These associations were more consistent and pronounced for women than men. Only SQ for fullness was associated with ad libitum test lunch energy intake in women. Similar but weaker relationships were found between appetite sensations and the 3-day self-reported energy intake. Weight loss was associated with changes in appetite sensations (p0.01) and the best predictors of body weight loss were fasting desire to eat; hunger; and PFC (p0.01). CONCLUSIONS: These results demonstrate that appetite sensations are relatively useful predictors of spontaneous energy intake, free-living total energy intake and body weight loss. They also confirm that SQ for fullness predicts energy intake, at least in women.

Noise robust voice activity detection using normal probability testing and time-domain histogram analysis

This paper presents a method of voice activity detection (VAD) suitable for high noise scenarios, based on the fusion of two complementary systems. The first system uses a proposed non-Gaussianity score (NGS) feature based on normal probability testing. The second system employs a histogram distance score (HDS) feature that detects changes in the signal through conducting a template-based similarity measure between adjacent frames. The decision outputs by the two systems are then merged using an open-by-reconstruction fusion stage. Accuracy of the proposed method was compared to several baseline VAD methods on a database created using real recordings of a variety of high-noise environments.

Histogram of weighted local directions for gait recognition

In this paper, we explore the effectiveness of patch-based gradient feature extraction methods when applied to appearance-based gait recognition. Extending existing popular feature extraction methods such as HOG and LDP, we propose a novel technique which we term the Histogram of Weighted Local Directions (HWLD). These 3 methods are applied to gait recognition using the GEI feature, with classification performed using SRC. Evaluations on the CASIA and OULP datasets show significant improvements using these patch-based methods over existing implementations, with the proposed method achieving the highest recognition rate for the respective datasets. In addition, the HWLD can easily be extended to 3D, which we demonstrate using the GEV feature on the DGD dataset, observing improvements in performance.

Histogram matching and global initialization for laser-only SLAM in large unstructured environments

This paper presents an enhanced algorithm for matching laser scan maps using histogram correlations. The histogram representation effectively summarizes a map's salient features such that pairs of maps can be matched efficiently without any prior guess as to their alignment. The histogram matching algorithm has been enhanced in order to work well in outdoor unstructured environments by using entropy metrics, weighted histograms and proper thresholding of quality metrics. Thus our large-scale scan-matching SLAM implementation has a vastly improved ability to close large loops in real-time even when odometry is not available. Our experimental results have demonstrated a successful mapping of the largest area ever mapped to date using only a single laser scanner. We also demonstrate our ability to solve the lost robot problem by localizing a robot to a previously built map without any prior initialization.

Tracking people in 3D using position, size and shape

This paper presents a prototype tracking system for tracking people in enclosed indoor environments where there is a high rate of occlusions. The system uses a stereo camera for acquisition, and is capable of disambiguating occlusions using a combination of depth map analysis, a two step ellipse fitting people detection process, the use of motion models and Kalman filters and a novel fit metric, based on computationally simple object statistics. Testing shows that our fit metric outperforms commonly used position based metrics and histogram based metrics, resulting in more accurate tracking of people.

Topological SLAM Using Fast Vision Techniques

In this paper we propose a method for vision only topological simultaneous localisation and mapping (SLAM). Our approach does not use motion or odometric information but a sequence of colour histograms from visited places. In particular, we address the perceptual aliasing problem which occurs using external observations only in topological navigation. We propose a Bayesian inference method to incrementally build a topological map by inferring spatial relations from the sequence of observations while simultaneously estimating the robot's location. The algorithm aims to build a small map which is consistent with local adjacency information extracted from the sequence measurements. Local adjacency information is incorporated to disambiguate places which otherwise would appear to be the same. Experiments in an indoor environment show that the proposed technique is capable of dealing with perceptual aliasing using visual observations only and successfully performs topological SLAM.

Dual-process action of exercise on appetite control : increase in orexigenic drive but improvement in meal-induced satiety

Background: Exercise could contribute to weight loss by altering the sensitivity of the appetite regulatory system. Objective: The aim of this study was to assess the effects of 12 wk of mandatory exercise on appetite control. Design: Fifty-eight overweight and obese men and women [mean (±SD) body mass index (in kg/m2) = 31.8 ± 4.5, age = 39.6 ± 9.8 y, and maximal oxygen intake = 29.1 ± 5.7 mL · kg–1 · min–1] completed 12 wk of supervised exercise in the laboratory. The exercise sessions were designed to expend 2500 kcal/wk. Subjective appetite sensations and the satiating efficiency of a fixed breakfast were compared at baseline (week 0) and at week 12. An Electronic Appetite Rating System was used to measure subjective appetite sensations immediately before and after the fixed breakfast in the immediate postprandial period and across the whole day. The satiety quotient of the breakfast was determined by calculating the change in appetite scores relative to the breakfast's energy content. Results: Despite large variability, there was a significant reduction in mean body weight (3.2 ± 3.6 kg), fat mass (3.2 ± 2.2 kg), and waist circumference (5.0 ± 3.2 cm) after 12 wk. The analysis showed that a reduction in body weight and body composition was accompanied by an increase in fasting hunger and in average hunger across the day (P < 0.0001). Paradoxically, the immediate and delayed satiety quotient of the breakfast also increased significantly (P < 0.05). Conclusions: These data show that the effect of exercise on appetite regulation involves at least 2 processes: an increase in the overall (orexigenic) drive to eat and a concomitant increase in the satiating efficiency of a fixed meal.

Low-fat oxidation may be a factor in obesity among men with schizophrenia

Objective: Obesity associated with atypical antipsychotic medications is an important clinical issue for people with schizophrenia. The purpose of this project was to determine whether there were any differences in resting energy expenditure (REE) and respiratory quotient (RQ) between men with schizophrenia and controls. Method: Thirty-one men with schizophrenia were individually matched for age and relative body weight with healthy, sedentary controls. Deuterium dilution was used to determine total body water and subsequently fat-free mass (FFM). Indirect calorimetry using a Deltatrac metabolic cart was used to determine REE and RQ. Results: When corrected for FFM, there was no significant difference in REE between the groups. However, fasting RQ was significantly higher in the men with schizophrenia than the controls. Conclusion: Men with schizophrenia oxidised proportionally less fat and more carbohydrate under resting conditions than healthy controls. These differences in substrate utilisation at rest may be an important consideration in obesity in this clinical group.

Enhancing an Incremental Clustering Algorithm for Web Page Collections

With the size and state of the Internet today, a good quality approach to organizing this mass of information is of great importance. Clustering web pages into groups of similar documents is one approach, but relies heavily on good feature extraction and document representation as well as a good clustering approach and algorithm. Due to the changing nature of the Internet, resulting in a dynamic dataset, an incremental approach is preferred. In this work we propose an enhanced incremental clustering approach to develop a better clustering algorithm that can help to better organize the information available on the Internet in an incremental fashion. Experiments show that the enhanced algorithm outperforms the original histogram based algorithm by up to 7.5%.

Towards an automatic road lane marks extraction based on ISODATA segmentation and shadow detection from large-scale aerial images

The automatic extraction of road features from remote sensed images has been a topic of great interest within the photogrammetric and remote sensing communities for over 3 decades. Although various techniques have been reported in the literature, it is still challenging to efficiently extract the road details with the increasing of image resolution as well as the requirement for accurate and up-to-date road data. In this paper, we will focus on the automatic detection of road lane markings, which are crucial for many applications, including lane level navigation and lane departure warning. The approach consists of four steps: i) data preprocessing, ii) image segmentation and road surface detection, iii) road lane marking extraction based on the generated road surface, and iv) testing and system evaluation. The proposed approach utilized the unsupervised ISODATA image segmentation algorithm, which segments the image into vegetation regions, and road surface based only on the Cb component of YCbCr color space. A shadow detection method based on YCbCr color space is also employed to detect and recover the shadows from the road surface casted by the vehicles and trees. Finally, the lane marking features are detected from the road surface using the histogram clustering. The experiments of applying the proposed method to the aerial imagery dataset of Gympie, Queensland demonstrate the efficiency of the approach.

Fusing shrinking and expanding active contour models for robust IRIS segmentation

The performance of iris recognition systems is significantly affected by the segmentation accuracy, especially in non- ideal iris images. This paper proposes an improved method to localise non-circular iris images quickly and accurately. Shrinking and expanding active contour methods are consolidated when localising inner and outer iris boundaries. First, the pupil region is roughly estimated based on histogram thresholding and morphological operations. There- after, a shrinking active contour model is used to precisely locate the inner iris boundary. Finally, the estimated inner iris boundary is used as an initial contour for an expanding active contour scheme to find the outer iris boundary. The proposed scheme is robust in finding exact the iris boundaries of non-circular and off-angle irises. In addition, occlusions of the iris images from eyelids and eyelashes are automatically excluded from the detected iris region. Experimental results on CASIA v3.0 iris databases indicate the accuracy of proposed technique.

Vision-based topological mapping and localisation

Competent navigation in an environment is a major requirement for an autonomous mobile robot to accomplish its mission. Nowadays, many successful systems for navigating a mobile robot use an internal map which represents the environment in a detailed geometric manner. However, building, maintaining and using such environment maps for navigation is difficult because of perceptual aliasing and measurement noise. Moreover, geometric maps require the processing of huge amounts of data which is computationally expensive. This thesis addresses the problem of vision-based topological mapping and localisation for mobile robot navigation. Topological maps are concise and graphical representations of environments that are scalable and amenable to symbolic manipulation. Thus, they are well-suited for basic robot navigation applications, and also provide a representational basis for the procedural and semantic information needed for higher-level robotic tasks. In order to make vision-based topological navigation suitable for inexpensive mobile robots for the mass market we propose to characterise key places of the environment based on their visual appearance through colour histograms. The approach for representing places using visual appearance is based on the fact that colour histograms change slowly as the field of vision sweeps the scene when a robot moves through an environment. Hence, a place represents a region of the environment rather than a single position. We demonstrate in experiments using an indoor data set, that a topological map in which places are characterised using visual appearance augmented with metric clues provides sufficient information to perform continuous metric localisation which is robust to the kidnapped robot problem. Many topological mapping methods build a topological map by clustering visual observations to places. However, due to perceptual aliasing observations from different places may be mapped to the same place representative in the topological map. A main contribution of this thesis is a novel approach for dealing with the perceptual aliasing problem in topological mapping. We propose to incorporate neighbourhood relations for disambiguating places which otherwise are indistinguishable. We present a constraint based stochastic local search method which integrates the approach for place disambiguation in order to induce a topological map. Experiments show that the proposed method is capable of mapping environments with a high degree of perceptual aliasing, and that a small map is found quickly. Moreover, the method of using neighbourhood information for place disambiguation is integrated into a framework for topological off-line simultaneous localisation and mapping which does not require an initial categorisation of visual observations. Experiments on an indoor data set demonstrate the suitability of our method to reliably localise the robot while building a topological map.