Humans use visual context to improve object recognition. Yet, many machine vision algorithms still focus on local object features, discarding surrounding features as unwanted clutter. Here we study the impact of learning contextual cues while training an object classifier. In a new image database with 10 object categories and 28,800 images, objects were presented in contextual or uniform backgr...

امروزه توسعه سیستم‌های کارآمد و اثربخش بهداشت درمان به یکی از نگرانی‌های اصلی دولت‌ها تصمیم‌گیرندگان تبدیل شده است. تأمین به‌موقع دارو کارایی سیستم موجودی توزیع، نقشی حیاتی در زنجیره بیمارستانی دارند برای افزایش این فعالیت‌ها، فرآیند تصمیم‌گیری مناسب مدیریت ضروری نظر می‌رسد. پژوهش یک مدل دوهدفه به‌صورت برنامه‌ریزی خطی عدد صحیح مختلط جریان اطلاعات ارائه اهداف حداقل­کردن هزینه‌های خرید، نگهداری، ...

نخستین گام در راستای تحلیل و ارزیابی ریسک‌های زنجیره تأمین شناسایی این ریسک‌ها است. روش‌های مرسوم بر اساس فیلترهای دستی یا خودکار داده‌­محور ارائه شده فیلتر به‌­دلیل محدودیت‌های نمونه‌گیری دارای مشکلات اعتبارسنجی هستند از طرف دیگر مبتنی داده، داده‌های ریسک که پیچیده مبهم هستند، عملکرد ضعیفی دارند. برای پرکرده خلل پژوهشی، پژوهش، چارچوبی تعاملی بین تحلیل‌­گر ماشین حجم وسیعی حوزه مواد غذایی با است...

In this paper we study the role of movement strategies during learning in object recognition models. We show that a simple model, the RBF, can outperform a more complex hierarchical model, the HMAX, when rotation and scale invariance are provided by the training phase. Moreover, we assess the exploitation of temporal information by the RBF using optic flow. The results show that the RBF model c...

Object detection and localization is a challenging task. Among several approaches, more recently hierarchical methods of feature-based object recognition have been developed and demonstrated high-end performance measures. Inspired by the knowledge about the architecture and function of the primate visual system, the computational HMAX model has been proposed. At the same time robust visual obje...

In this paper we address the comparison of two feature matching techniques which can be integrated in the HMAX framework. This comparison involves the originally proposed MAX technique and the histogram technique originating from Bag-of-Words literature. We have found that each of these techniques have their own field of operation. The histogram technique clearly outperforms the MAX technique w...

The paper presents a method of image recognition, which is inspired by research in visual cortex. The architecture of our model called CaNN is similar to the one proposed in neocognitron, LeNet or HMAX networks. It is composed of many consecutive layers with various number of planes (receptive fields). Units in the corresponding positions of the planes in one layer receive input from the same r...

Our Breadth-First Heuristic Search Planner (BFHSP) is a domain-independent STRIPS planner that finds sequential plans that are optimal with respect to the number of actions it takes to reach a goal. We developed BFHSP as part of our research on space-efficient graph search. It uses breadth-first search since we found that breadth-first search is more efficient than best-first search when divide...