With the recent surge in YouTube usage, there has been a proliferation of user-generated videos where individuals evaluate cosmetics. Consequently, many companies are increasingly utilizing evaluation videos for their product marketing and market research. However, a notable drawback is the manual classification of these product review videos incurring significant costs and time. Therefore, this paper proposes a deep learning-based cosmetics search algorithm to automate this task. The algorithm consists of two networks: One for detecting candidates in images using shape features such as circles, rectangles, etc and Another for filtering and categorizing these candidates. The reason for choosing a Two-Stage architecture over One-Stage is that, in videos containing background scenes, it is more robust to first detect cosmetic candidates before classifying them as specific objects. Although Two-Stage structures are generally known to outperform One-Stage structures in terms of model architecture, this study opts for Two-Stage to address issues related to the acquisition of training and validation data that arise when using One-Stage. Acquiring data for the algorithm that detects cosmetic candidates based on shape and the algorithm that classifies candidates into specific objects is cost-effective, ensuring the overall robustness of the algorithm.

목적 : 대상의 내적정보 중 라인드로잉이 대상 재인에 미치는 효과를 알아보고 형태항상성에 중요한 역할을 한다는 사실을 제시하고자 한다. 연구방법 : 실험은 물리적 대응과제를 사용하였으며, 나중에 제시되는 대상은 먼저 제시되는 대상과 같거나 다르다. 즉, 연속적으로 제시되는 두 대상이 물리적으로 동일한 대상인지를 신속․정확하게 판단하는 데 있어서 대상의 제시방식과 회전 정도의 효과를 비교하였다. 결과 : 결과는 모두 라인드로잉이 대상재인에 효과적인 것으로 나타났다. 라인드로잉이 윤곽보다 신속․정확하게 재인되었으며, 음영과는 차이를 보이지 않았다. 그리고 물리적 대응에서는 대상의 회전 정도가 재인의 정확도와 반응시간에 영향을 미쳤다. 특히 방향이 가장 압축된 모양으로 제시되었을 경우에 라인드로잉의 효과는 더욱 명확한 것으로 나타났다. 결론 : 대상재인과 형태항상성에 대상의 내적 정보가 긍정적인 영향을 제공한다. 그 중 라인드로잉은 대상재인에 긍정적인 속성이자 단서이다.

Recently, smart factories have attracted much attention as a result of the 4th Industrial Revolution. Existing factory automation technologies are generally designed for simple repetition without using vision sensors. Even small object assemblies are still dependent on manual work. To satisfy the needs for replacing the existing system with new technology such as bin picking and visual servoing, precision and real-time application should be core. Therefore in our work we focused on the core elements by using deep learning algorithm to detect and classify the target object for real-time and analyzing the object features. We chose YOLO CNN which is capable of real-time working and combining the two tasks as mentioned above though there are lots of good deep learning algorithms such as Mask R-CNN and Fast R-CNN. Then through the line and inside features extracted from target object, we can obtain final outline and estimate object posture.

영상 인식 기술은 평면 영상에 대해서 많이 연구되고 그 성능 또한 발전하고 있다. 그러나 평면 영상이 아닌 구면 파노라마 영상과 다양한 환경에서 주어지는 특수한 형태의 영상에 대한 인식은 평면과 다르게 기하학적인 왜곡으로 인해서 많은 어려움이 따른다. 본 논문에서는 평면영상의 인식 기술에서 최근 각광받는 훈련을 통한 신경망 인식 기법이 구면 파노라마 영상의 인식에서도 쓰일 수 있음을 보인다. 또한 구면 영상에 대한 기존 신경망 모델의 인식률을 높이기 위해서 큐브맵 변환을 활용하는 방법을 제시한다.

본 논문은 마커를 사용하지 않고 현실 객체 대상 모바일 증강현실 게임의 일반적인 제작 과정에 대해서 기술하고 있다. 본 논문에서는 모바일 환경에서의 성능 최적화를 위해서 slam 기술을 사용하여 만들어진 포인트 클라우드 데이터를 별도의 편집툴을 사용하여 편집하였다. 또한 게임 실행단계에서 특징점 추출 및 디스크립터 매칭으로 인해 많은 부하가 발생하는데, 이를 줄이기 위해서 이전 입력 영상에서 매칭된 특징점에 대한 위치 추적을 위해 Opticalflow 추적을 사용하였다.

This paper presents a method of improving the pose recognition accuracy of objects by using Kinect sensor. First, by using the SURF algorithm, which is one of the most widely used local features point algorithms, we modify inner parameters of the algorithm for efficient object recognition. The proposed method is adjusting the distance between the box filter, modifying Hessian matrix, and eliminating improper key points. In the second, the object orientation is estimated based on the homography. Finally the novel approach of Auto-scaling method is proposed to improve accuracy of object pose estimation. The proposed algorithm is experimentally tested with objects in the plane and its effectiveness is validated.

This paper proposes an underwater localization algorithm using probabilistic object recognition. It is organized as follows; 1) recognizing artificial objects using imaging sonar, and 2) localizing the recognized objects and the vehicle using EKF(Extended Kalman Filter) based SLAM. For this purpose, we develop artificial landmarks to be recognized even under the unstable sonar images induced by noise. Moreover, a probabilistic recognition framework is proposed. In this way, the distance and bearing of the recognized artificial landmarks are acquired to perform the localization of the underwater vehicle. Using the recognized objects, EKF-based SLAM is carried out and results in a path of the underwater vehicle and the location of landmarks. The proposed localization algorithm is verified by experiments in a basin.

This paper presents a new shape-based algorithm using affine category shape model for object category recognition and model learning. Affine category shape model is a graph of interconnected nodes whose geometric interactions are modeled using pairwise potentials. In its learning phase, it can efficiently handle large pose variations of objects in training images by estimating 2-D homography transformation between the model and the training images. Since the pairwise potentials are defined on only relative geometric relationship between features, the proposed matching algorithm is translation and in-plane rotation invariant and robust to affine transformation. We apply spectral matching algorithm to find feature correspondences, which are then used as initial correspondences for RANSAC algorithm. The 2-D homography transformation and the inlier correspondences which are consistent with this estimate can be efficiently estimated through RANSAC, and new correspondences also can be detected by using the estimated 2-D homography transformation. Experimental results on object category database show that the proposed algorithm is robust to pose variation of objects and provides good recognition performance.

스테레오 정합은 스테레오 시각 분야에서 가장 활발히 연구되는 분야이다. 본 논문에서는 물체의 위치 인식을 위한 유전 알고리즘을 이용한 스테레오 정합을 제안한다. 정합 환경을 최적화 문제로 간주하고 진화 전략을 이용하여 최적해를 탐색한다. 따라서, 유전 연산자는 스테레오 정합에 맞게 설계하였고 개체는 변위집단을 대표한다. 영상의 수평화소라인을 염색체로 간주하였다. 비용함수는 스테레오 정합에서 사용하는 일반적인 제약조건들의 조합이다. 비용함수가 명암도, 유사도, 변위 평활성으로 구성되었기 때문에 정합을 시도할 때 매 세대마다 이 모든 요소들을 한번에 다룬다. 염색체를 정의하기 위해 LoG연산자로 경계선을 추출하였으며 실험을 통하여 제안한 방법을 검증하였다.

In this paper, we present a practical palce and object recognition method for guiding visitors in building environments. Recognizing palces or objects in real world can be a difficult problem due to motion blur and camera noise. In this work, we present a modeling method based on the bidirectional interactionbetween places and objects for simulataneous reinforcement for the robust recognition. The unification of visual context including scene context, object context, and temporal context is also. The proposed system has been tested to guide visitors in a large scale building environment(10 topological places, 80 3D objects)

In this paper, we introduce visual contexts in terms of types and utilization methods for robust object recognition with intelligent mobile robots. One of the core technologies for intelligent robots is visual object recognition. Robust techniques are strongly required since there are many sources of visual variations such as geometric, photometric, and noise. For such requirements, we define spatial context, hierarchical context, and temporal context. According to object recognition domain, we can select such visual contextx. We also propose a unified framework which can utilize the whole contexts and validates it in real working environment. Finally, we also discuss the furture research directions of object recognition technologies for intelligent robots.