A top-down manner-based DCNN architecture for semantic image segmentation

<div><p>Given their powerful feature representation for recognition, deep convolutional neural networks (DCNNs) have been driving rapid advances in high-level computer vision tasks. However, their performance in semantic image segmentation is still not satisfactory. Based on the analysis of visual mechanism, we conclude that DCNNs in a bottom-up manner are not enough, because semantic image segmentation task requires not only recognition but also visual attention capability. In the study, superpixels containing visual attention information are introduced in a top-down manner, and an extensible architecture is proposed to improve the segmentation results of current DCNN-based methods. We employ the current state-of-the-art fully convolutional network (FCN) and FCN with conditional random field (DeepLab-CRF) as baselines to validate our architecture. Experimental results of the PASCAL VOC segmentation task qualitatively show that coarse edges and error segmentation results are well improved. We also quantitatively obtain about 2%-3% intersection over union (IOU) accuracy improvement on the PASCAL VOC 2011 and 2012 test sets.</p></div

Examples that our method based on DBSCAN superpixels produced better results than the FCN model.

<p><b>Different colors represent different classes.</b> (a) Input image (b) Segmentation results from FCN (c) Segmentation results from FCN-DBSCAN (d) FCN-DBSCAN-v2 (e) Ground truth.</p

Superpixel segmentation results from the GS method.

<p><b>Different colors represent different superpixels.</b> (a) Input image (b) Superpixels from GS method (c) Semantic labels (d) Superpixels from GS method with semantic labels.</p

Performance of our proposed models on the PASCAL VOC 2011 and 2012 test sets compared to other state-of-art methods.

<p>Performance of our proposed models on the PASCAL VOC 2011 and 2012 test sets compared to other state-of-art methods.</p

Evaluation results of the PASCAL VOC 2012 test set.

<p>Evaluation results of the PASCAL VOC 2012 test set.</p

Coarse semantic segmentation results of the PASCAL VOC dataset based on the FCN and DeepLab-CRF model.

<p><b>Different colors represent different classes.</b> (a) Input image (b) Segmentation results from FCN (first two rows) and DeepLab-CRF (last two rows) (c) Ground truth.</p

Examples that our method based on GS superpixels produced better results than the FCN model.

<p><b>Different colors represent different classes.</b> (a) Input image (b) Segmentation results from FCN (c) Segmentation results from FCN-GS (d) FCN-GS-v2 (e) Ground truth.</p

Analysis of psychotherapy sessions with GAI via computer methods

The aim of the presented thesis is to find out the answer for the question of how it is possible to process and evaluate by computer methods of data coming from psychotherapy sessions using the method of Guided Affective Imagery. The Guided Affective Imagery is a relatively young psychotherapy approach based on the European tradition of psychodynamic psychology which presumes that in assignment relatively indefinite motive in imagination during day dream comes to manifestation of a client's unconsciousness contents and conflicts, defensive mechanisms, resistance, motives and fantasy. The biofeedback device by which was the rate of heartbeat used during the whole session and computer programs for quantitative text analysis were used for verifying the usage of computer methods for the sessions' analysis. The quantitative text analysis focused on identification of primarily and secondarily measure of the item process in the preliminary part of a session, during the phase of imagination and at the final part of the session by two code systems. In a certain way the results indicate complementary progress of changes in the primary and secondary process when the highest representation of secondary process expressions was identified in the introductory part of the session whereas the highest ratio of..

Image reconstruction of the Moby phantom from noisy projection dataset.

<p>Rows from the top to the bottom are the reconstructed results from three groups of projections with different noise levels. The photon number N₀ of noisy case 1, 2, 3 are 5×10⁶, 2×10⁶, and 5×10⁵, respectively. From left to right in each row, results of the FBP, TV-ADM, TpV-ADM, TGV-ADM, and TGpV-ADM methods are presented.</p

The selected characteristics of the criminal career and chronic offender of a violent crime

The thesis: The selected characteristic of the criminal career and chronic offender of a violent crime is divided into two parts: theoretical and empirical part. The theoretical part deals with risk factors that can lead to criminal career, psychological characteristics of chronic offenders and offenders of violent crime. Finally, theoretical part mentioned the concept of criminal careers with basic elements: participation, frequency, duration, starts a criminal career, the interval between of the offenses, the remaining length and finishing of the criminal career, specialization or universality, severity and escalation, accomplice to criminal career. The theoretical part also summarizes the basic statistics on the crime of chronic offender. The empirical part is the quantitative retrospective study of the forensic expertises in the field of clinical psychology. The main task of the thesis focused on the assessment of escalation - increased severity of crimes during individual criminal career of the chronic offender of violent crime. The basic hypothesis is: The length of the first and last imprisonment sentence is not differing. In terms of qualitative description of escalation of violent crime, I examined the first and last type of aggression, the first and last degree of violence and the first..