282 research outputs found

    Statistical image fusion with generalised Gaussian and Alpha-Stable distributions

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    Multiscale color-texture image segmentation with adaptive region merging

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    Statistical multiscale image segmentation via Alpha-stable modeling

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    Is “plausibility” a core feature of obsessions?

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    Letters to the Editors.[Excerpt] Obsessions are unwanted, intrusive, recurrent, and persistent ideas, thoughts, images, or impulses that cause intense anxiety and are recognized as self-generated. The word obsession derives from the Latin word obsidere, which means being occupied, preoccupied, or taken into possession. In his General psychopathology, Karl Jaspers identified essential characteristics of obsessional symptoms, including a nonsensical and absurd quality, compelling force of thoughts, the belief that thoughts can influence events, need for order, and unacceptable impulses.1 Later, ego-dystonia and insight were identified as core features of obsessions, crucial for differential diagnosis between obsessive-compulsive disorder (OCD), obsessivecompulsive personality disorder (OCPD), and psychotic disorders.2 Ego-dystonia reflects the conflict (or disharmony) between an idea, a thought, an impulse, an image, or an act and the subject and/or the subject’s self-image. Insight is the extent of knowledge that one has about one’s own thoughts and acts. [...](undefined)info:eu-repo/semantics/publishedVersio

    Statistical model-based fusion of noisy multi-band images in the wavelet domain

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    Segmentation-driven image fusion based on alpha-stable modeling of wavelet coefficients

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    Complex wavelet domain image fusion based on fractional lower order moments

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    A novel lane feature extraction algorithm implemented on the TMS320DM6437 DSP platform

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    The effect of pixel-level fusion on object tracking in multi-sensor surveillance video

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    This paper investigates the impact of pixel-level fusion of videos from visible (VIZ) and infrared (IR) surveillance cameras on object tracking performance, as compared to tracking in single modality videos. Tracking has been ac-complished by means of a particle filter which fuses a colour cue and the structural similarity measure (SSIM). The highest tracking accuracy has been obtained in IR se-quences, whereas the VIZ video showed the worst track-ing performance due to higher levels of clutter. How-ever, metrics for fusion assessment clearly point towards the supremacy of the multiresolutional methods, especially Dual Tree-Complex Wavelet Transform method. Thus, a new, tracking-oriented metric is needed that is able to ac-curately assess how fusion affects the performance of the tracker. 1
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