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Landscapes

為了解決Car Camera的問題，作者Wood, Tom (PHT) 這樣論述：

These three volumes of Tom Wood's new work, Landscapes, are drawn from the artist's extensive unseen and unpublished landscape work. The first volume concentrates on Wood's photographs made in response to County Mayo in the west of Ireland--the landscape of his birthplace and childhood and an area h

e has returned to as an artist almost every year since 1975. Taken over decades, views of this wild and remote landscape--many of them glimpsed from the car, bus or train during his journeys there--are combined with fragile fragments of surviving family photographs, video stills, and intimate and af

fectionate portraits of day-to-day life within a rural community. The second volume consists of Wood's landscapes predominantly made within Merseyside, where he lived and worked for 25 years, from 1978-2003. In this more urban environment, his landscapes encompass pictures of people's homes and gard

ens, parks, wastelands and the river Mersey. Wood moved to Wales in 2003 to address what he has referred to as the matter of landscape. His open and experimental approach to photography means he is constantly pushing its formal and conceptual possibilities. Selected from the photographs he has been

making in Wales, the third volume is the most formally abstract of the three books and includes many photographs taken with a panoramic camera--complex, optically rich pictures with multiple points of view and focus.

Car Camera進入發燒排行的影片

基於語意之輪廓表示法及全連結捲積類神經網路之單晶片多車輛辨識系統

為了解決Car Camera的問題，作者徐雋航 這樣論述：

鑒於現今智慧車輛發展迅速，前方車輛辨識及車距檢測為先進駕駛輔助系統 (Advanced Driver Assistance Systems, ADAS) 設計中相當重要的一環，此項技術通常藉由攝影鏡頭擷取前方影像，並透過影像辨識技術來判斷前方是否存在車輛、障礙物等等，進而控制車輛減速以保持安全距離。而這些複雜的圖形辨識技術往往需要透過高功耗之大型運算系統來實現，並且，若將傳統電腦安裝於車內常需要克服體積過大、耐震性不佳等缺點。因此，本研究專注於如何將車輛辨識及車距檢測演算法實現於單晶片，以達到高性能、低功耗，以及體積小之目的。為實現前方車輛辨識及車距檢測，本研究透過單一彩色相機模組收集前方影

像資訊，並於單一現場可程式邏輯閘陣列 (Field Programmable Gate Array, FPGA) 晶片中以最精簡之硬體電路實現白平衡 (White Balance)、影像對比度強化技術 (Image Contrast Technique)、物體邊緣檢測、利用基於模糊語意影像描述 (Semantics-based Vague Image Representation, SVIR) 改良之基於語義之輪廓表示法 (Semantic-based Contour Representation, SCR) 特徵表達物體、再透過不同的卷積核 (Convolution Kernel) 重釋SC

R特徵並交由全連接類神經網路(Fully Connected Neural Network, FCN) 進行車輛辨識。最後，以多個邊界框 (Bounding Box) 同時檢測前方多台車輛，達到單頁多目標辨識 (Single Shot MultiBox Detector，SSD) 之功能，而邊界框之座標可以透視法 (Perspective View) 計算前車相對距離。根據本研究之實驗結果，在相機以每秒90張影像攝影速度以及影像解析度在640×480像素的條件下，本研究僅須3.61us即可完成單台車輛辨識，車輛辨識率可達到94%，且車輛與非車輛至少保持38%以上之分離度，有效減少感測錯誤的情況

發生。因此，實現一真正高性能、低功耗以及體積小之前方車輛辨識晶片。

I Keep Trying to Catch His Eye: A Memoir of Loss, Grief, and Love

為了解決Car Camera的問題，作者Maisel, Ivan 這樣論述：

＂In February 2015, Ivan Maisel received a call that would alter his life forever: his son Max’s car was found abandoned in a parking next to Lake Ontario. Two months later, Max’s body would be found in the lake ... [This] is the story of Maisel’s love for a son who was so different from him, but

who he loved so deeply, and how he came to learn that grief for Max was nothing more than a last, ultimate expression of love. Navigating the moments of their complicated relationship, as well as their love each other, Maisel explores the bridges he tried to build to his son and the grief that engul

fed him and his family after Max’s death by suicide. Taking its title from Max’s love of photography--and his tendency to only love the camera when he was behind it, looking away whenever his picture was taken--I Keep Trying to Catch His Eye delves into the tragically transformative reality of losin

g a child, all with grace, depth, and refinement. But by humanizing Max and humanizing his grief, Maisel evokes understanding instead of sorrow, appreciation instead of anxiety--and love instead of fear＂--

基於FPGA單晶片及像素趨勢車道線檢測法實現車道線感測系統之研究

為了解決Car Camera的問題，作者廖國欽 這樣論述：

車輛自動駕駛系統目前主要是由自動跟車 (Adaptive Cruise Control, ACC) 以及車道偏離警示 (Lane Departure Warning System, LDWS) 兩大系統所組成。然而，自動跟車系統在實現過程中，由於必須藉由前方車輛實現車輛跟隨功能，因此若無前方車輛時則無法實現此功能。反觀車道偏離警示系統是依據車道線軌跡來幫助車輛保持於車道內，因此具備較高實用性。在此，本研究特別針對車道感測進行研究。由於傳統的車道線感測必須仰賴高效率的電腦才能有效地完成運算，為了克服傳統車道線辨識的缺點，本研究專注於如何將車道線辨識演算法簡化，並實現在單晶片上，達到低功耗之目的

。本研究以單一數位相機及單一現場可程式邏輯閘陣列 (Field Programmable Gate Array, FPGA) 實線以精簡之硬體電路達到即時於白天及黃昏情況下進行車道線辨識。透過像素趨勢車道檢測法 (Pixel Trend Lane Detection, PTLD) 擷取特徵，並將所得之車道位置利用線性回歸 (Linear Regression, LR) 決定車道線的軌跡，再透過左右車道回歸線取得車道的中心線，藉此引導車輛穩定行駛於車道中。另外，本研究還搭配語音辨識擴充模組 (DFR0177 Voice Recognition) 來辨識由Google Map路線規劃所傳出的語音指

令。根據辨識的結果，輸出行車指令給FPGA，以此決定車輛轉彎或直線行車路線模式。根據本研究之實驗結果，在使用每秒90張畫面播放速度以及640×480影像解析度情況下，只需11 ms即可擷取車道線特徵。而由左右車道線線性回歸決定出的中心線與實際影像中的中心線，誤差僅在5個像素以內。故本研究不管在運算速度以及準確度上均符合實際運用需求，未來可以有效幫助車輛穩定行駛於車道，達成自動駕駛之目的。