Crowd Counting Using Multiple Local Features

In public venues, crowd size is a key indicator of crowd safety and stability. Crowding levels can be detected using holistic image features, however this requires a large amount of training data to capture the wide variations in crowd distribution. If a crowd counting algorithm is to be deployed across a large number of cameras, such a large and burdensome training requirement is far from ideal. In this paper we propose an approach that uses local features to count the number of people in each foreground blob segment, so that the total crowd estimate is the sum of the group sizes. This results in an approach that is scalable to crowd volumes not seen in the training data, and can be trained on a very small data set. As a local approach is used, the proposed algorithm can easily be used to estimate crowd density throughout different regions of the scene and be used in a multi-camera environment. A unique localised approach to ground truth annotation reduces the required training data is also presented, as a localised approach to crowd counting has different training requirements to a holistic one. Testing on a large pedestrian database compares the proposed technique to existing holistic techniques and demonstrates improved accuracy, and superior performance when test conditions are unseen in the training set, or a minimal training set is used.

Tests of the elaborated intrusion theory of craving and desire : features of alcohol craving during treatment for an alcohol disorder

Objectives. We tested predictions from the elaborated intrusion (EI) theory of desire, which distinguishes intrusive thoughts and elaborations, and emphasizes the importance of imagery. Secondarily, we undertook preliminary evaluations of the Alcohol Craving Experience (ACE) questionnaire, a new measure based on EI Theory. Methods. Participants (N ¼ 232) were in correspondence-based treatment trials for alcohol abuse or dependence. The study used retrospective reports obtained early in treatment using the ACE, and daily self-monitoring of urges, craving, mood and alcohol consumption. Results. The ACE displayed high internal consistency and test – retest reliability and sound relationships with self-monitored craving, and was related to Baseline alcohol dependence, but not to consumption. Imagery during craving was experienced by 81%,with 2.3 senses involved on average. More frequent imagery was associated with longer episode durations and stronger craving. Transient intrusive thoughts were reported by 87% of respondents, and were more common if they frequently attempted to stop alcohol cognitions. Associations between average daily craving and weekly consumption were seen. Depression and negative mood were associated with more frequent, stronger and longer lasting desires for alcohol. Conclusions. Results supported the distinction of automatic and controlled processes in craving, together with the importance of craving imagery. They were also consistent with prediction of consumption from cross-situational averages of craving, and with positive associations between craving and negative mood. However, this study’s retrospective reporting and correlational design require that its results be interpreted cautiously. Research using ecological momentary measures and laboratory manipulations is needed before confident inferences about causality can be made.

Selecting, optimizing and fusing ‘salient’ Gabor features for facial expression recognition

This paper describes a novel framework for facial expression recognition from still images by selecting, optimizing and fusing ‘salient’ Gabor feature layers to recognize six universal facial expressions using the K nearest neighbor classifier. The recognition comparisons with all layer approach using JAFFE and Cohn-Kanade (CK) databases confirm that using ‘salient’ Gabor feature layers with optimized sizes can achieve better recognition performance and dramatically reduce computational time. Moreover, comparisons with the state of the art performances demonstrate the effectiveness of our approach.

Social networking : investigating the features of Facebook application

Research on social networking sites like Facebook is emerging but sparse. The exploratory study investigates the value users derive from self-described ‘cool’ Facebook applications, and explores the features that either encourage or discourage users to recommend application to their friends. Thus the concepts of value and cool are explored in a social networking setting. Our qualitative data shows that consumers derive a combination of functional value along with either social or emotional value from the applications. Female Facebook users indicated self-expression as important, while mates then to use Facebook application to socially compete. Three broad categories emerged for application features; symmetrical features can both encourage or discourage recommendation, asymmetrical features one encourage or discourage but not both, and polar features where different levels of the same feature encourage or discourage. Recommending or not recommending an application tends to be the result of a combination of features rather than one feature in isolation.

Human brain regions required for the dividing and switching of attention between two features of a single object

This combined PET and ERP study was designed to identify the brain regions activated in switching and divided attention between different features of a single object using matched sensory stimuli and motor response. The ERP data have previously been reported in this journal [64]. We now present the corresponding PET data. We identified partially overlapping neural networks with paradigms requiring the switching or dividing of attention between the elements of complex visual stimuli. Regions of activation were found in the prefrontal and temporal cortices and cerebellum. Each task resulted in different prefrontal cortical regions of activation lending support to the functional subspecialisation of the prefrontal and temporal cortices being based on the cognitive operations required rather than the stimuli themselves.

Cascading appearance-based features for visual speaker verification

The cascading appearance-based (CAB) feature extraction technique has established itself as the state of the art in extracting dynamic visual speech features for speech recognition. In this paper, we will focus on investigating the effectiveness of this technique for the related speaker verification application. By investigating the speaker verification ability of each stage of the cascade we will demonstrate that the same steps taken to reduce static speaker and environmental information for the speech recognition application also provide similar improvements for speaker recognition. These results suggest that visual speaker recognition can improve considerable when conducted solely through a consideration of the dynamic speech information rather than the static appearance of the speaker's mouth region.

Structural and cellular features in metaphyseal and diaphyseal periosteum of osteoporotic rats

Despite the important physiological role of periosteum in the pathogenesis and treatment of osteoporosis, little is known about the structural and cellular characteristics of periosteum in osteoporosis. To study the structural and cellular differences in both diaphyseal and metaphyseal periosteum of osteoporotic rats, samples from the right femur of osteoporotic and normal female Lewis rats were collected and tissue sections were stained with hematoxylin and eosin, antibodies or staining kit against tartrate resistant acid phosphatase (TRAP), alkaline phosphatase (ALP), vascular endothelial growth factor (VEGF), von Willebrand (vWF), tyrosine hydroxylase (TH) and calcitonin gene-related peptide (CGRP). The results showed that the osteoporotic rats had much thicker and more cellular cambial layer of metaphyseal periosteum compared with other periosteal areas and normal rats (P\0.001). The number of TRAP? osteoclasts in bone resorption pits, VEGF? cells and the degree of vascularization were found to be greater in the cambial layer of metaphyseal periosteum of osteoporotic rats (P\0.05), while no significant difference was detected in the number of ALP? cells between the two groups. Sympathetic nerve fibers identified by TH staining were predominantly located in the cambial layer of metaphyseal periosteum of osteoporotic rats. No obvious difference in the expression of CGRP between the two groups was found. In conclusion, periosteum may play an important role in the cortical bone resorption in osteoporotic rats and this pathological process may be regulated by the sympathetic nervous system.

Choice of image features for depth-axis control in image based visual servo control

This paper is concerned with choosing image features for image based visual servo control and how this choice influences the closed-loop dynamics of the system. In prior work, image features tend to be chosen on the basis of image processing simplicity and noise sensitivity. In this paper we show that the choice of feature directly influences the closed-loop dynamics in task-space. We focus on the depth axis control of a visual servo system and compare analytically various approaches that have been reported recently in the literature. The theoretical predictions are verified by experiment.

Exploring visual features through Gabor representations for facial expression detection

Gabor representations have been widely used in facial analysis (face recognition, face detection and facial expression detection) due to their biological relevance and computational properties. Two popular Gabor representations used in literature are: 1) Log-Gabor and 2) Gabor energy filters. Even though these representations are somewhat similar, they also have distinct differences as the Log-Gabor filters mimic the simple cells in the visual cortex while the Gabor energy filters emulate the complex cells, which causes subtle differences in the responses. In this paper, we analyze the difference between these two Gabor representations and quantify these differences on the task of facial action unit (AU) detection. In our experiments conducted on the Cohn-Kanade dataset, we report an average area underneath the ROC curve (A`) of 92.60% across 17 AUs for the Gabor energy filters, while the Log-Gabor representation achieved an average A` of 96.11%. This result suggests that small spatial differences that the Log-Gabor filters pick up on are more useful for AU detection than the differences in contours and edges that the Gabor energy filters extract.