標(biāo)題: Titlebook: Boosting-Based Face Detection and Adaptation; Cha Zhang Book 2010 Springer Nature Switzerland AG 2010 [打印本頁(yè)] 作者: Melanin 時(shí)間: 2025-3-21 16:04
書(shū)目名稱(chēng)Boosting-Based Face Detection and Adaptation影響因子(影響力)
書(shū)目名稱(chēng)Boosting-Based Face Detection and Adaptation影響因子(影響力)學(xué)科排名
書(shū)目名稱(chēng)Boosting-Based Face Detection and Adaptation網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱(chēng)Boosting-Based Face Detection and Adaptation網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Boosting-Based Face Detection and Adaptation被引頻次
書(shū)目名稱(chēng)Boosting-Based Face Detection and Adaptation被引頻次學(xué)科排名
書(shū)目名稱(chēng)Boosting-Based Face Detection and Adaptation年度引用
書(shū)目名稱(chēng)Boosting-Based Face Detection and Adaptation年度引用學(xué)科排名
書(shū)目名稱(chēng)Boosting-Based Face Detection and Adaptation讀者反饋
書(shū)目名稱(chēng)Boosting-Based Face Detection and Adaptation讀者反饋學(xué)科排名
作者: Jogging 時(shí)間: 2025-3-21 23:12
Cascade-based Real-Time Face Detection, and Maydt, 2002) used manual tuning or heuristics to set the intermediate rejection thresholds for the detector, which is inefficient and suboptimal. Recently, various approaches has been proposed to address this issue. Notably, Bourdev and Brandt (Bourdev and Brandt, 2005) proposed a method for se作者: 爭(zhēng)議的蘋(píng)果 時(shí)間: 2025-3-22 03:31
Multiple Instance Learning for Face Detection,ion results surrounding the ground truth rectangle are plausible. Such an observation is indeed quite general. In many object recognition tasks, it is often extremely tedious to generate large training sets of objects because it is not easy to specify exactly where the objects are. For instance, giv作者: 星球的光亮度 時(shí)間: 2025-3-22 04:47
Detector Adaptation,l-known that the performance of such a learned classifier will depend heavily on the representativeness of the labeled data used during training. If the training data contains only a small number of examples sampled in a particular test environment, the learned classifier may be too specific to be g作者: WITH 時(shí)間: 2025-3-22 10:01 作者: 榮幸 時(shí)間: 2025-3-22 12:57
LBS and TeleCartography II: About the bookWe have focused on face detection almost exclusively in the previous chapters. In this chapter, we will present two other applications of boosting learning. These two applications extend the above algorithms in two ways: the learning algorithm itself, and the features being used for learning.作者: Allowance 時(shí)間: 2025-3-22 20:16 作者: 一個(gè)攪動(dòng)不安 時(shí)間: 2025-3-22 22:09
Conclusions and FutureWork,ne learning literature, such as the confidence rated boosting (Schapire and Singer, 1999), the statistical view of boosting (Friedman et al., 1998), the AnyBoost framework (Mason et al., 2000), which views boosting as a gradient decent process, and the general idea of multiple instance learning (Nowlan and Platt, 1995).作者: Acetaminophen 時(shí)間: 2025-3-23 03:12 作者: 付出 時(shí)間: 2025-3-23 08:14 作者: 自然環(huán)境 時(shí)間: 2025-3-23 11:21 作者: FUSC 時(shí)間: 2025-3-23 14:42 作者: Gorilla 時(shí)間: 2025-3-23 18:12
Sustainable Development Goals Series location and extent of each face (Yang et al., 2002). While this appears as a trivial task for human beings, it is a very challenging task for computers and it has been one of the most heavily studied research topics in the past few decades. The difficulty associated with face detection can be attr作者: liposuction 時(shí)間: 2025-3-24 01:37 作者: 變色龍 時(shí)間: 2025-3-24 02:32 作者: 樹(shù)木中 時(shí)間: 2025-3-24 07:36 作者: Condescending 時(shí)間: 2025-3-24 12:39 作者: DNR215 時(shí)間: 2025-3-24 18:18
Localizing the SDGs in African Cities. Sochman-Matas (Sochman and Matas, 2005) used a ratio test to determine the rejection thresholds.While this has statistical validity, distributions must be estimated, which introduces empirical risk. This is a particular problem for the first few rejection thresholds, and it can lead to low detecti作者: intelligible 時(shí)間: 2025-3-24 23:01 作者: Mnemonics 時(shí)間: 2025-3-25 01:20 作者: 向宇宙 時(shí)間: 2025-3-25 07:13
Book 2010s detectors that are both fast and accurate. We then present two multiple instance learning schemesfor face detection, multiple instance learning boosting (MILBoost) and winner-take-all multiple category boosting (WTA-McBoost). MILBoost addresses the uncertainty in accurately pinpointing the locatio作者: surmount 時(shí)間: 2025-3-25 11:28 作者: 座右銘 時(shí)間: 2025-3-25 12:55
Book 2010s approaches to face detection developed in the past decade, with more emphasis on boosting-based learning algorithms. We then present a series of algorithms that are empowered by the statistical view of boosting and the concept of multiple instance learning. We start by describing a boosting learni作者: ARCH 時(shí)間: 2025-3-25 15:59
A Formal Model for Mobile Map Adaptationen a ZIP code of handwritten digits, which pixel is the location of a “5”? This sort of ambiguity leads to training sets which themselves have high error rates, which limits the accuracy of any trained classifier.作者: 現(xiàn)任者 時(shí)間: 2025-3-25 23:04
2153-1056 iew various approaches to face detection developed in the past decade, with more emphasis on boosting-based learning algorithms. We then present a series of algorithms that are empowered by the statistical view of boosting and the concept of multiple instance learning. We start by describing a boost作者: MAG 時(shí)間: 2025-3-26 03:05
Sustainable Development Goals Seriesers and it has been one of the most heavily studied research topics in the past few decades. The difficulty associated with face detection can be attributed to many variations in skin color, scale, location, orientation (in-plane rotation), pose (out-of-plane rotation), facial expression, lighting conditions, occlusions, etc., as seen in Fig. 1.1.作者: 紳士 時(shí)間: 2025-3-26 06:05 作者: –DOX 時(shí)間: 2025-3-26 11:06
A Brief Survey of the Face Detection Literature,ers and it has been one of the most heavily studied research topics in the past few decades. The difficulty associated with face detection can be attributed to many variations in skin color, scale, location, orientation (in-plane rotation), pose (out-of-plane rotation), facial expression, lighting conditions, occlusions, etc., as seen in Fig. 1.1.作者: 大雨 時(shí)間: 2025-3-26 15:48
Detector Adaptation,he training data contains only a small number of examples sampled in a particular test environment, the learned classifier may be too specific to be generalized to unseen data. On the other hand, if the training data is extensive, the classifier may generalize well but perform sub-optimally in a particular test environment.作者: nocturnal 時(shí)間: 2025-3-26 20:17 作者: SNEER 時(shí)間: 2025-3-26 22:12 作者: 左右連貫 時(shí)間: 2025-3-27 02:40 作者: 功多汁水 時(shí)間: 2025-3-27 07:53 作者: 保全 時(shí)間: 2025-3-27 12:49 作者: Intercept 時(shí)間: 2025-3-27 17:37
Book 2020is common in empirical software engineering..The book helps entrepreneurs and practitioners to become aware of various phenomena, challenges, and practices that occur in real-world startups, and provides insights based on sound research methodologies presented in a simple and easy-to-read manner. It作者: sleep-spindles 時(shí)間: 2025-3-27 20:04
Static and Dynamic Determinants of Left Ventricular Chamber Stiffness and Fillingn). This distinction is based on the notion that change in the static factors evolve very slowly, while dynamic factors may change from moment to moment. It should be emphasized, however, that these factors are interdependent and the effect of any single mechanisms is difficult to isolate and evalua作者: Graphite 時(shí)間: 2025-3-27 21:56 作者: Gastric 時(shí)間: 2025-3-28 04:09
Large—Scale Structure from Galaxy and Cluster Surveyssults on the power spectrum and two-point correlation correlation function from the 2dF and REFLEX surveys, highlighting the advantage of X-ray clusters in the comparison to cosmological models, given their easy-to-understand mass selection function. I also mention recent measurements of the matter
