The Victory over death

Abstract—Using high-resolution flat panel displays for TV systems, the visibility of signal impairments has grown. As a solution, content-based image processing can be used for obtaining higher levels of picture-quality improvement, thereby outperforming traditional TV image-enhancement methods. This paper presents a new algorithm and feature model for blue sky detection, which enables content-adaptive enhancement of TV video sequences. The algorithm analyzes the image, creates adaptive position and color models, and classifies the sky areas of the image using a pixel-accurate soft segmentation. Such a probabilistic measure matches well with the requirements of typical video enhancement functions in TVs. We have evaluated the proposal for typical and critical natural scenes, and obtained a clear improvement over state-of-the-art techniques. I. INTRODUCTION

"Would you stll remember me ?"

Currently, the parameter settings of TV image processing algorithms are often globally constant or adapted to some simple local pictorial features. This leads to a sub-optimal picture quality, as compared to a system that locally adapts the processing to the content of the image. Such content adaptation can be enabled if video objects of interest are detected, and image areas belonging to each object are segmented [1]. Sky is an example of visually important objects. The smooth appearance of sky makes noise and other artifacts clearly visible in these regions. Besides picture-quality improvement, sky detection can also be used for 3D depth-map generation, and for semantic-level applications such as content-based image and video retrieval. Fig. 2 shows an example of segmentation-based image enhancement. A first proposal on sky detection [2],[3], calculates a "sky belief map", followed by a hard decision on connected areas for the final sky detection. A second method [4],[5] assumes that sky regions are smooth and are found around the top of the image. Here, an initial sky probability is calculated based on color, texture and vertical position, and the features of highly probably areas are used to compute a final sky probability. While the first method yields useful results in annotating sky regions, its segmentation result lacks the spatial consistency due to the crisp classification decision. The second method is suitable for video applications, but due to modeling simplicity often results in false detections. Neural control of vergence eye movements: convergence and divergence neurons in midbrain 1. L. E. Mays Abstract Animals with binocular single vision use disjunctive (vergence) eye movements to align the two eyes on a visual target. Several lines of evidence suggest that conjugate and vergence eye movement commands are generated independently and combined at the medial rectus motoneurons. If this were true, then a pure vergence eye-position signal should exist. This signal would be proportional to the horizontal angle between the eyes (vergence angle), without regard to the direction of conjugate gaze. The purpose of this experiment was to identify and study neurons that carry a pure vergence signal. Extracellular unit recordings were made from midbrain and pontine sites in monkeys trained to track visual targets moving in the horizontal, vertical, and depth (or target vergence) planes. The most commonly encountered neuron that had a vergence signal was the convergence cell. These units had a firing rate that was linearly proportional to the convergence angle; their activity was unaffected by changes in conjugate gaze. Changes in convergence cell activity preceded the change in vergence angle slightly. Convergence cell activity increased for increased convergence regardless of whether the change was in response to purely accommodative or disparity cues. Divergence cells were found far less frequently. These cells were similar to convergence cells except that they decreased their firing rate for increases in convergence. The activity of divergence cells was unaffected by changes in the direction of conjugate gaze. Both convergence and divergence cells were found, intermixed, in the mesencephalic reticular formation must outside the oculomotor nucleus. Most cells with a vergence signal were found within 1-2 mm of the nucleus. These results support the view that conjugate and vergence signals are generated independently and are combined at the extraocular motoneurons. Convergence cells seem ideally suited to provide the vergence signal required by the nearby medial rectus motoneurons. Moving Average Convergence-Divergence (MACD) Introduction Developed by Gerald Appel in the late seventies, the Moving Average Convergence-Divergence (MACD) indicator is one of the simplest and most effective momentum indicators available. The MACD turns two trend-following indicators, moving averages, into a momentum oscillator by subtracting the longer moving average from the shorter moving average. As a result, the MACD offers the best of both worlds: trend following and momentum. The MACD fluctuates above and below the zero line as the moving averages converge, cross and diverge. Traders can look for signal line crossovers, centerline crossovers and divergences to generate signals. Because the MACD is unbounded, it is not particularly useful for identifying overbought and oversold levels.

Note: MACD can be pronounced as either "MAC-DEE" or "M-A-C-D." Here is an example chart with the MACD indicator in the lower panel: ABSTRACT The postsynaptic actions of acetylcholine, adenosine, yaminobutyric acid, histamine, norepinephrine, and serotonin were analyzed in human cortical pyramidal cells maintained in vitro. The actions of these six putative neurotransmitters converged onto three distinct potassium currents. Application of acetylcholine, histamine, norepinephrine, or serotonin all increased spiking by reducing spike-frequency adaptation, in part by reducing the current that underlies the slow afterhyperpolarization. In addition, application of muscarinic receptor agonists to all neurons or of serotonin to middle-layer cells substantially reduced or blocked the Mcurrent (a K+ current that is voltage and time dependent). Inhibition of neuronal firing was elicited by adenosine, baclofen (a yaminobutyric acid type B receptor agonist), or serotonin and appeared to be due to an increase in the same potassium current by all three agents. These data reveal that individual neuronal currents in the human cerebral cortex are under the control of several putative neurotransmitters and that each neurotransmitter may exhibit more than one postsynaptic action. The specific anatomical connections of these various neurotransmitter systems, as well as their heterogeneous distribution of postsynaptic receptors and responses, allows each to make a specific contribution to the modulation of cortical activity. Human cerebral cortical activity may be under the influence of a large number of neuroactive substances, including acetylcholine (ACh), adenosine, 'y-aminobutyric acid (GABA), histamine, norepinephrine, and serotonin (5-HT) (1-8). The postsynaptic actions of these putative neurotransmitters in human neocortical neurons are largely unknown, although the demonstration of muscarinic receptor-mediated block of the voltage- and time-dependent K+ current known as Mcurrent (IM) is a notable exception (2). Investigations of neurotransmitter actions in nonhuman subcortical neurons have revealed a wide variety of postsynaptic responses as well as a remarkable convergence and divergence of neurotransmitter action (9-22). For example, individual neurons in the rodent hippocampus, thalamus, and substantia nigra respond to more than one putative neurotransmitter with the same ionic response (12, 17, 20-22). Conversely, a single neurotransmitter, such as acetylcholine, can elicit markedly different ionic responses in separate brain regions and even in distinct morphological cell classes in the same nucleus (e.g., refs. 15 and 19). Convergence and divergence of neurotransmitter action has important implications for understanding functional systems in the brain. Not only do the neuronal systems underlying behavior consist of a number of transmitter pathways, but each pathway is involved in more than one functional system (e.g., ref. 22). In addition, many neurological disorders กิจกรรม ของมนุษย์ที่ เยื่อหุ้มสมอง สมอง อาจ จะอยู่ภายใต้ อิทธิพลของ ของจำนวนมาก ของสาร neuroactive รวมทั้ง acetylcholine ( ACh ) adenosine , กรด y- aminobutyric ( GABA ) กระ norepinephrine และ serotonin ( 5 -HT ) ( 1-8 ) การกระทำ ของสารสื่อประสาท postsynaptic สมมุติ เหล่านี้ใน เซลล์ประสาท neocortical มนุษย์ เป็นที่รู้จัก ส่วนใหญ่ แม้ว่า release ofACh, norepinephrine, 5-HT, and/or histamine may also lead to an increase in baseline firing rate, although this excitation will be much less than the enhancement of phasic barrages of EPSPs because of the marked activation voltage dependence of IAHp and IM. Furthermore, reduction of these two currents will have much less effect on inhibitory postsynaptic potentials or unitary EPSPs because neither of these results in substantial activation of IAHP or IMP Convergence and divergence of transmitter actions in the human cerebral cortex complicates our understanding of the control and modulation of neuronal activity. It is likely that in the natural state cortical pyramidal cells are under the constant influence of a dynamically changing array of neuroactive substances. Additive and nonadditive interactions among these substances may allow for subtleties in neuromodulation that could not otherwise occur. Understanding these actions and interactions may facilitate the development of more specific pharmacological therapies for neurological disorders, such as epilepsy, Alzheimer disease, and agerelated cognitive decline. We thank Dr. Dennis Spencer for his collaboration in this project and the patients for their informed consent to the use of the tissue. This work was supported by a grant from the National Institute of Neurological and Communicative Disorders and Stroke, fellowships from the Klingenstein Foundation (D.A.M.), and the American Epilepsy Foundation (A.W.), and by the Jacob Javits Center in Neuroscience. 1. Palacios, J. M., Probst, A. & Cortes, R. (1986) Trends Neurosci. 9, 284-289. 2. Halliwell, J. V. (1986) Neurosci. Lett. 67, 1-6. 3. Pazos, A., Probst, A. & Palacois, J. M. (1987) Neuroscience 21, 97-122. 4. Fastbom, J., Pazos, A., Probst, A. &