Experience of Pleasure and Emotional Expression in Individuals with Schizotypal Personality Features

Difficulties in feeling pleasure and expressing emotions are one of the key features of schizophrenia spectrum conditions, and are significant contributors to constricted interpersonal interactions. The current study examined the experience of pleasure and emotional expression in college students who demonstrated high and low levels of schizotypal personality disorder (SPD) traits on self-report questionnaires. One hundred and seventeen subjects with SPD traits and 116 comparison controls were recruited to participate. Cluster analyses conducted in the SPD group identified negative SPD and positive SPD subgroups. The negative SPD group exhibited deficient emotional expression and anticipatory pleasure, but showed intact consummatory pleasure. The positive SPD group reported significantly greater levels of anticipatory, consummatory and total pleasure compared to the control group. Both SPD groups reported significantly more problems in everyday memory and greater levels of depressive and anxiety-related symptoms

Schizophrenia and the Scaffolded Self

This is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this recordA family of recent externalist approaches in philosophy of mind argues that our psychological capacities are synchronically and diachronically “scaffolded” by external (i.e., beyond-the-brain) resources. Despite much interest in this topic, however, it has not found its way to philosophy of psychiatry in a substantive way. I here consider how these “scaffolded” approaches to mind and self might inform debates in phenomenological psychopathology. First, I introduce the idea of “affective scaffolding”. I distinguish three forms of affective scaffolding and support this taxonomy by appealing to different sources of empirical work. Second, I put the idea of affective scaffolding to work. Using schizophrenia as a case study, I argue — along with others in phenomenological psychopathology — that schizophrenia is fundamentally a self-disturbance. However, I offer a subtle reconfiguration of these approaches. I argue that schizophrenia is not simply a disruption of ipseity or minimal self-consciousness but rather a disruption of the scaffolded self, established and regulated via its ongoing engagement with the world and others. I conclude that this way of thinking about the scaffolded self is potentially transformative both for our theoretical as well as practical understanding of the causes and character of schizophrenic experience, insofar as it suggests the need to consider new forms of intervention and treatment

Predictive Saliency Maps for Surveillance Videos

pp 508-513International audienceWhen viewing video sequences, the human visual system (HVS) tends to focus on the active objects. These are perceived as the most salient regions in the scene. Additionally, human observers tend to predict the future positions of moving objects in a dynamic scene and to direct their gaze to these positions. In this paper we propose a saliency detection model that accounts for the motion in the sequence and predicts the positions of the salient objects in future frames. This is a novel technique for attention models that we call Predictive Saliency Map (PSM). PSM improves the consistency of the estimated saliency maps for video sequences. PSM uses both static information provided by static saliency maps (SSM) and motion vectors to predict future salient regions in the next frame. In this paper we focus only on surveillance videos therefore, in addition to low-level features such as intensity, color and orientation we consider high-level features such as faces as salient regions that attract naturally viewers attention. Saliency maps computed based on these static features are combined with motion saliency maps to account for saliency created by the activity in the scene. The predicted saliency map is computed using previous saliency maps and motion information. The PSMs are compared with the experimentally obtained gaze maps and saliency maps obtained using approaches from the literature. The experimental results show that our enhanced model yields higher ability to predict eye fixations in surveillance videos

Salient objects detection in dynamic scenes using color and texture features

International audienceVisual saliency is an important research topic in the field of computer vision due to its numerous possible applications. It helps to focus on regions of interest instead of processing the whole image or video data. Detecting visual saliency in still images has been widely addressed in literature with several formulations. However , visual saliency detection in videos has attracted little attention, and is a more challenging task due to additional temporal information. A common approach for obtaining a spatio-temporal saliency map is to combine a static saliency map and a dynamic saliency map. In our work, we model the dynamic textures in a dynamic scene with local binary patterns to compute the dynamic saliency map, and we use color features to compute the static saliency map. Both saliency maps are computed using a bio-inspired mechanism of human visual system with a discriminant formulation known as center surround saliency, and are fused in a proper way. The proposed model has been extensively evaluated with diverse publicly available datasets which contain several videos of dynamic scenes, and comparison with state-of-the art methods shows that it achieves competitive results

Characterization by Hyperspectral Imaging and Hypercolor Gamut Estimation for Structural Color Prints

International audienceA recently developed color printing system on glass plates, based on dot-by-dot laser irradiation generating the growth of metallic nanoparticles in a special coating, produces structural colors depending strongly on the illumination and observation configuration. The difficulty for an exhaustive color characterization of the printing technology comes not only from the goniochromaticity of the samples, but also from their very high specularity, to which classical measurement instruments are not adapted. Moreover, as the light-matter interaction relies on a number of optical phenomena (surface plasmon resonance, interferences , diffraction, effects of polarization of light) for which no predictive model is available today, their characterization requires measurement of many printed samples. In this paper, we present a characterization method based on multispec-tral imaging and on spectral prediction for halftone colors that permitted a first gamut estimation in three specific illumination/viewing configurations. Recent progresses in nanotechnologies enable the coloration of glass with interesting visual rendering. This is for example the case of the technology developed by the la-boratoire Hubert Curien, called PICSULP [1], where a coating containing silver [2] is deposited on the glass plate, then irradiated by a laser beam in order to anneal the coating and cluster the metallic ions into metallic nanoparticles (NPs). Goniochromatic col-oration of the glass plate surface is thus obtained thanks to various optical phenomena: the presence of silver NPs generates surface plasmon resonance, therefore wavelength-selective absorption as in stained glass [3]; the organization of the NPs along one plane parallel to the coating-air interface generates interferences as in thin films; the NPs can even be aligned along parallel lines, as shown in Figure 1-a, which produces diffraction effects visible at grazing angles, and also gives to the sample a dichroic spectral behavior , i.e. polarization sensitive colors [4,5]. These optical effects are influenced by several physical parameters: the nanoparticle shape, size and spatial organization, as wel

Detecting text in natural scenes based on a reduction of photometric effects: problem of color invariance

International audienceIn this paper, we propose a novel method for detecting and segmenting text layers in complex images. This method is robust against degradations such as shadows, non-uniform illumination, low-contrast, large signal-dependent noise, smear and strain. The proposed method first uses a geodesic transform based on a morphological reconstruction technique to remove dark/light structures connected to the borders of the image and to emphasize on objects in center of the image. Next uses a method based on difference of gamma functions approximated by the Generalized Extreme Value Distribution (GEVD) to find a correct threshold for binarization. The main function of this GEVD is to find the optimum threshold value for image binarization relatively to a significance level. The significance levels are defined in function of the background complexity. In this paper, we show that this method is much simpler than other methods for text binarization and produces better text extraction results on degraded documents and natural scene images