Review: Object vision in a structured world

In natural vision, objects appear at typical locations, both with respect to visual space (e.g., an airplane in the upper part of a scene) and other objects (e.g., a lamp above a table). Recent studies have shown that object vision is strongly adapted to such positional regularities. In this review we synthesize these developments, highlighting that adaptations to positional regularities facilitate object detection and recognition, and sharpen the representations of objects in visual cortex. These effects are pervasive across various types of high-level content. We posit that adaptations to real-world structure collectively support optimal usage of limited cortical processing resources. Taking positional regularities into account will thus be essential for understanding efficient object vision in the real world

Challenges in video based object detection in maritime scenario using computer vision

This paper discusses the technical challenges in maritime image processing and machine vision problems for video streams generated by cameras. Even well documented problems of horizon detection and registration of frames in a video are very challenging in maritime scenarios. More advanced problems of background subtraction and object detection in video streams are very challenging. Challenges arising from the dynamic nature of the background, unavailability of static cues, presence of small objects at distant backgrounds, illumination effects, all contribute to the challenges as discussed here

Adrenal Myelolipoma: Diagnosis and Management

Normal 0 false false false EN-US X-NONE AR-SA MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial; mso-bidi-theme-font:minor-bidi;} Introduction: Adrenal myelolipomas are benign lesions that contain hematopoietic and fatty elements. They are usually hormonally inactive and asymptomatic until they reach large sizes. With the routine use of cross-sectional imaging, these lesions are now being discovered with increasing frequency. Materials and Methods: We performed a comprehensive review of the literature using the PubMed database containing the key word adrenal myelolipoma. Results: We identified 492 articles written from 1956 to 2006 and reviewed 93 in detail including the authors’ own experience. In this review, we highlighted the salient diagnostic features of adrenal myelolipomas and offered a guide for management of these benign lesions. Conclusion: Adrenal myelolipomas may grow over time, but they can usually be followed without surgical excision. In some cases, very large myelolipomas can present with pain and can be confused with necrotic adrenal carcinomas, thus necessitating their surgical removal.</span

A robust approach to differentiate human monocyte-derived microglia from peripheral blood mononuclear cells

Microglia are implicated in most neurodegenerative diseases. Here, we present a robust and efficient protocol to differentiate monocyte-derived microglia-like cells (MDMi) from whole blood. The protocol consists of three parts. The first part will describe two methods for PBMC isolation. This will be followed by MDMi differentiation, and lastly, the characterization of MDMi by immunocytochemistry. MDMi can be used to investigate microglial-related responses in various age-related neurodegenerative diseases and can be applied to drug testing on a personalized basis. For complete details on the use and execution of this protocol, please refer to Quek et al

An objective based classification of aggregation techniques for wireless sensor networks

Wireless Sensor Networks have gained immense popularity in recent years due to their ever increasing capabilities and wide range of critical applications. A huge body of research efforts has been dedicated to find ways to utilize limited resources of these sensor nodes in an efficient manner. One of the common ways to minimize energy consumption has been aggregation of input data. We note that every aggregation technique has an improvement objective to achieve with respect to the output it produces. Each technique is designed to achieve some target e.g. reduce data size, minimize transmission energy, enhance accuracy etc. This paper presents a comprehensive survey of aggregation techniques that can be used in distributed manner to improve lifetime and energy conservation of wireless sensor networks. Main contribution of this work is proposal of a novel classification of such techniques based on the type of improvement they offer when applied to WSNs. Due to the existence of a myriad of definitions of aggregation, we first review the meaning of term aggregation that can be applied to WSN. The concept is then associated with the proposed classes. Each class of techniques is divided into a number of subclasses and a brief literature review of related work in WSN for each of these is also presented