CAJ | 학술논문

　　目标检测式内容放大方法可以对图片的内容采取不同的关注度，并且能够维持图像的整体效果。当检测出目标区域后，在对图像进行操作时，能够保持该区域内容的连贯性，使该区域的内容有明显的放大效果。利用基于方向梯度直方图（HOG）的目标检测算法在多尺度空间中提取图像的特征信息，并将所得的HOG描述符通过线性支持向量机（SVM）进行识别分类后，利用非最大化抑制算法对所得的多个边界框做融合处理得到包含目标物体的最优边界框，再用权值函数M改变边界框相关区域内各像素点的能量值，通过移除一些与周围区域相关度小的接缝，从而使目标区域内容达到更合理的放大效果。
　　목표검측식내용방대방법가이대도편적내용채취불동적관주도，병차능구유지도상적정체효과。당검측출목표구역후，재대도상진행조작시，능구보지해구역내용적련관성，사해구역적내용유명현적방대효과。이용기우방향제도직방도（HOG）적목표검측산법재다척도공간중제취도상적특정신식，병장소득적HOG묘술부통과선성지지향량궤（SVM）진행식별분류후，이용비최대화억제산법대소득적다개변계광주융합처리득도포함목표물체적최우변계광，재용권치함수M개변변계광상관구역내각상소점적능량치，통과이제일사여주위구역상관도소적접봉，종이사목표구역내용체도경합리적방대효과。
Target-detective content amplification method can make different attentions to the content of photograph, and mean-while maintain the integrity of the image. When detecting the target region, in the image operating process, it can maintain the regional content consistency, and make the content of the region have obvious amplifying effect. It uses the detection algorithm based on the Histograms of Oriented Gradients(HOG)to extract image characteristics information in multi-scale space, puts the HOG descriptor into linear Support Vector Machine(SVM)which as a classifier for recognition, uses the non-maximum sup-pression algorithm to fuse these bounding boxes to get the best bounding box of the target object, then uses weights function(M) to change the energy of each pixel in the area of bounding box, removes some seams which have less correlation with the sur-rounding area, lets the regional content reach more reasonable amplification effect.