Around the corner的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列包括價格和評價等資訊懶人包

Bali Pocket Precincts: A Pocket Guide to the Island’’s Best Cultural Hangouts, Shops, Bars and Eateries

為了解決Around the corner的問題，作者Pace, Alison 這樣論述：

Bali Pocket Precincts is your curated guide to Bali’s best cultural, shopping, spa and dining experiences. Bali is known for its tumbling rice paddies, colorful culture and world-class surf. But delve deeper and you’ll discover that every area of the island has its own distinctive personality. Ar

tistic Ubud is home to centuries-old temples, yoga shalas galore and some of the most inventive plant-based restaurants in the world. On the south coast, Seminyak is all about designer boutiques and vibey beach clubs with swim-up bars and sunset soundtracks. Head north and volcano bagging, rice-terr

ace trekking and exceptional scuba diving all become possibilities. Yet on top of all this, Bali’s popularity as a holiday destination is due, at least in part, to the remarkably warm nature of the locals. Their magical living culture is palpable around every corner, from the incense-infused offerin

gs to the elaborate temple ceremonies. And tourists are welcomed with open arms. As well as detailed reviews and maps for major attractions through to hidden gems, Bali Pocket Precincts includes a selection of field trips that encourage you to get off the beaten path and visit areas further afield i

ncluding North Bali and the neighboring island of Lombok. Slip this guide into your pocket and head off on an adventure, experiencing the most fascinating sights and surrounds like a local.

Around the corner進入發燒排行的影片

藉由利用興趣點周圍的二進制特徵圖對加速穩健特徵演算法進行改進之研究

為了解決Around the corner的問題，作者賴炫翔 這樣論述：

過去一段時間以來，加速穩健特徵 (Speed-Up Robust Feature, SURF)演算法是一種非常流行的方法，用於開發應用程序，例如圖像匹配、深度圖估計、全景拼接、對象檢測、跟踪、導航等。儘管如此，為了滿足更高級應用對更高精度和實時處理速度的需求，許多研究工作中在提高匹配精度的同時降低SURF演算法的計算複雜度。加速穩健特徵演算法具有很高的計算複雜度，尤其是在興趣點匹配階段。該階段使用算術運算，佔據整體運算時間的94%。本論文提出一種興趣點匹配演算法。此演算法利用了興趣點周圍的統計特性資訊轉換為二進制特徵圖來重新定義興趣點描述子並於興趣點匹配階段中以邏輯運算的方式來降低運算複雜度

且透過統計特性來加速過濾不合理之興趣點。實驗結果表明本演算法相對適用於立體影像對。此演算法相對於加速穩健特徵演算法平均運算時間降低了約55%且平均興趣點匹配率上升約14%，但興趣點匹配正確率下降約17%。至於圖像的縮放、旋轉與裁切的測試中，在微小的變化下本演算法仍然可以有效執行。

The Toolbox: Methods and Mindsets for Maximizing Social Impact

為了解決Around the corner的問題，作者Harold, Jacob 這樣論述：

Transform your corner of the world with strategies from a social change visionary In The Toolbox: Strategies for Crafting Social Impact, celebrated nonprofit executive Jacob Harold delivers an expert guide to doing good in the 21st century. In the book, you’ll explore nine tools that have driven

world-shaking social movements and billion-dollar businesses--tools that can work just as well for a farmers market or fire department or small business.The author describes each of the tools--including storytelling, mathematical modeling, and design thinking--in a stand-alone chapter, intertwining

each with a consistent narrative and full-color visual structure. Readers will also find: A consistent focus and emphasis on the work of social good and how it can be applied in any business, government agency, or nonprofit organizationDozens of poems, photos, equations, diagrams, and stories to ill

ustrate and enrich of the core ideas of the book.A fulsome, three-chapter introduction offering an a crash course in the basics of social impact strategy in the 21st centuryA comprehensive strategic playbook for contributing to the shared work of building a better worldAn essential blueprint for any

one interested in improving the world around them, The Toolbox: Strategies for Crafting Social Impact is an incisive strategic guide that will prove to be indispensable for everyone who seeks to collaboratively build something better.

一個用於步態辨識與攜帶物狀態分類的帶有注意力模組的身分資訊提取方法

為了解決Around the corner的問題，作者唐士閎 這樣論述：

步態辨識是一種基於影像視覺的辨識方法，可以應用於人流監控、犯罪調查、門禁控制、逃犯偵測等社會安全問題上，現有的步態辨識方法中，往往會受到行人的協變量（如大型衣物、包包等）影響，使得行人輪廓變形，影響傳統基於步態輪廓影像辨識身分的方法。在本文中，我們提出了一個身分辨識網路，我們基於既有的研究成果，組合了解糾纏模組、共享注意力模組及攜帶物狀態分類器，利用解糾纏模組來分離出身分特徵及協變量特徵，共享注意力模組來消弱協變量資訊對身分資訊提取過程的影響，使得身分提取過程更穩健。我們使用網路提取之身分特徵用於身分辨識並使用協變量特徵來分辨人的攜帶物狀態，可進一步在身分辨識之外獲知兩個行人影像攜帶狀態的差

異。本研究的攜帶物狀態分類器基於OU-LP-Bag資料集訓練，可以將行人攜帶物狀態細分為7類，獲取這樣的資訊可以用於增強對於視訊內容的理解，對於實際應用有更好的擴展。過程中藉由加強對於協變量特徵內容訊息的提取，可以加強解糾纏任務在分離身分特徵及協變量特徵的精確度。最後，本論文基於OU-LP-Bag資料集來進行身分驗證效能的實驗，經本論文改良後的架構其辨識效能可與目前一流研究相匹配。